Australia’s NationalScience Agency P18#y2 Assessment of the potentialecological outcomes of waterresource development in theSouthern Gulfcatchments Areport from the CSIROSouthern Gulf CatchmentsRiver Water Resource Assessmentfor the National Water Grid Rocio Ponce Reyes1,Danial Stratford1, Simon Linke1,Linda Merrin1,Rob Kenyon1, Rik Buckworth1,2, Roy Aijun Deng1,Justin Hughes1,Heather McGinness1,Jodie Pritchard1, Lynn Seo1, NathanWaltham3 1 CSIRO,2Charles DarwinUniversity,3James Cook University P24#y1 P24#y2 ISBN 978-1-4863-2073-8 (print) ISBN 978-1-4863-2074-5 (online) Citation Ponce Reyes R, Stratford D, Linke S, Merrin L, Kenyon R, Buckworth R, Deng RA, Hughes J, McGinness H, Pritchard J, Seo L and Waltham N (2024) Assessment of the potential ecological outcomes of water resource development in the Southern Gulf catchments. A technical report from the CSIRO Southern Gulf Water Resource Assessment for the National Water Grid. CSIRO, Australia. Copyright © Commonwealth Scientific and Industrial Research Organisation 2024. To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO. Important disclaimer CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it. CSIRO is committed to providing web accessible content wherever possible. If you are having difficulties with accessing this document please contact Email CSIRO Enquiries . CSIRO Southern Gulf Water Resource Assessment acknowledgements This report was funded through the National Water Grid’s Science Program, which sits within the Australian Government’s Department of Climate Change, Energy, the Environment and Water. Aspects of the Assessment have been undertaken in conjunction with the Northern Territory and Queensland governments. The Assessment was guided by two committees: i. The Governance Committee: CRC for Northern Australia/James Cook University; CSIRO; National Water Grid (Department of Climate Change, Energy, the Environment and Water); Northern Land Council; NT Department of Environment, Parks and Water Security; NT Department of Industry, Tourism and Trade; Office of Northern Australia; Queensland Department of Agriculture and Fisheries; Queensland Department of Regional Development, Manufacturing and Water ii. The Southern Gulf catchments Steering Committee: Amateur Fishermen’s Association of the NT; Austral Fisheries; Burketown Shire; Carpentaria Land Council Aboriginal Corporation; Health and Wellbeing Queensland; National Water Grid (Department of Climate Change, Energy, the Environment and Water); Northern Prawn Fisheries; Queensland Department of Agriculture and Fisheries; NT Department of Environment, Parks and Water Security; NT Department of Industry, Tourism and Trade; Office of Northern Australia; Queensland Department of Regional Development, Manufacturing and Water; Southern Gulf NRM Responsibility for the Assessment’s content lies with CSIRO. The Assessment’s committees did not have an opportunity to review the Assessment results or outputs prior to their release. The ecology team received great support from a large number of people in the Northern Territory Government and associated agencies. They provided access to files and reports, spatial and other data, species and habitat information and they also provided the team with their professional expertise and encouragement. For the Northern Territory, this was led by Simon Cruikshank. People in private industry, universities, local government and other organisations also helped us with parts of this work and advice, including Keller Kopf, Erica Garcia and Osmar Luiz. The work would not have been possible without the contributions and assistance from Cuan Petheram, Matt Gibbs, Caroline Bruce, Fazlul Karim, Shawn Kim, Steve Marvanek, Steve Gao, Jackie O’Sullivan, Carmel Pollino, Adam Liedloff, Jane Thomas and Darran King. This report was reviewed by Dr Jackie O’Sullivan and Dr Cuan Petheram (both CSIRO). Useful comments from Dr Carmel Pollino and Dr Adam Liedloff (both CSIRO) were also incorporated. Acknowledgement of Country CSIRO acknowledges the Traditional Owners of the lands, seas and waters, of the area that we live and work on across Australia. We acknowledge their continuing connection to their culture and pay our respects to their elders past and present. Photo Nicholson River. Source: CSIRO Director’s foreword Sustainable development and regional economic prosperity are priorities for the Australian, Queensland and Northern Territory (NT) governments. However, more comprehensive information on land and water resources across northern Australia is required to complement local information held by Indigenous Peoples and other landholders. Knowledge of the scale, nature, location and distribution of likely environmental, social, cultural and economic opportunities and the risks of any proposed developments is critical to sustainable development. Especially where resource use is contested, this knowledge informs the consultation and planning that underpin the resource security required to unlock investment, while at the same time protecting the environment and cultural values. In 2021, the Australian Government commissioned CSIRO to complete the Southern Gulf Water Resource Assessment. In response, CSIRO accessed expertise and collaborations from across Australia to generate data and provide insight to support consideration of the use of land and water resources in the Southern Gulf catchments. The Assessment focuses mainly on the potential for agricultural development, and the opportunities and constraints that development could experience. It also considers climate change impacts and a range of future development pathways without being prescriptive of what they might be. The detailed information provided on land and water resources, their potential uses and the consequences of those uses are carefully designed to be relevant to a wide range of regional-scale planning considerations by Indigenous Peoples, landholders, citizens, investors, local government, and the Australian, Queensland and NT governments. By fostering shared understanding of the opportunities and the risks among this wide array of stakeholders and decision makers, better informed conversations about future options will be possible. Importantly, the Assessment does not recommend one development over another, nor assume any particular development pathway, nor even assume that water resource development will occur. It provides a range of possibilities and the information required to interpret them (including risks that may attend any opportunities), consistent with regional values and aspirations. All data and reports produced by the Assessment will be publicly available. Chris Chilcott P52#yIS1 Project Director The Southern Gulf Water Resource Assessment Team Project Director Chris Chilcott Project Leaders Cuan Petheram, Ian Watson Project Support Caroline Bruce, Seonaid Philip Communications Emily Brown, Chanel Koeleman, Jo Ashley, Nathan Dyer Activities Agriculture and socio- economics Tony Webster, Caroline Bruce, Kaylene Camuti1, Matt Curnock, Jenny Hayward, Simon Irvin, Shokhrukh Jalilov, Diane Jarvis1, Adam Liedloff, Stephen McFallan, Yvette Oliver, Di Prestwidge2, Tiemen Rhebergen, Robert Speed3, Chris Stokes, Thomas Vanderbyl3, John Virtue4 Climate David McJannet, Lynn Seo Ecology Danial Stratford, Rik Buckworth, Pascal Castellazzi, Bayley Costin, Roy Aijun Deng, Ruan Gannon, Steve Gao, Sophie Gilbey, Rob Kenyon, Shelly Lachish, Simon Linke, Heather McGinness, Linda Merrin, Katie Motson5, Rocio Ponce Reyes, Jodie Pritchard, Nathan Waltham5 Groundwater hydrology Andrew R. Taylor, Karen Barry, Russell Crosbie, Margaux Dupuy, Geoff Hodgson, Anthony Knapton6, Stacey Priestley, Matthias Raiber Indigenous water values, rights, interests and development goals Pethie Lyons, Marcus Barber, Peta Braedon, Petina Pert Land suitability Ian Watson, Jenet Austin, Bart Edmeades7, Linda Gregory, Ben Harms10, Jason Hill7, Jeremy Manders10, Gordon McLachlan, Seonaid Philip, Ross Searle, Uta Stockmann, Evan Thomas10, Mark Thomas, Francis Wait7, Peter Zund Surface water hydrology Justin Hughes, Matt Gibbs, Fazlul Karim, Julien Lerat, Steve Marvanek, Cherry Mateo, Catherine Ticehurst, Biao Wang Surface water storage Cuan Petheram, Giulio Altamura8, Fred Baynes9, Jamie Campbell11, Lachlan Cherry11, Kev Devlin4, Nick Hombsch8, Peter Hyde8, Lee Rogers, Ang Yang Note: Assessment team as at September, 2024. All contributors are affiliated with CSIRO unless indicated otherwise. Activity Leaders are underlined. 1James Cook University; 2DBP Consulting; 3Badu Advisory Pty Ltd; 4Independent contractor; 5 Centre for Tropical Water and Aquatic Ecosystem Research. James Cook University; 6CloudGMS; 7NT Department of Environment, Parks and Water Security; 8Rider Levett Bucknall; 9Baynes Geologic; 10QG Department of Environment, Science and Innovation; 11Entura Shortened forms SHORT FORM FULL FORM AEP annual exceedance probability AWRA-R Australian Water Resources Assessment – River EOS end-of-system EPBC Act Environment Protection and Biodiversity Conservation Act 1999 IUCN International Union for Conservation of Nature Units UNIT DESCRIPTION ML megalitre d day y year t tonne GL gigalitre m metre km kilometre ha hectare ppt parts per thousand Preface Sustainable development and regional economic prosperity are priorities for the Australian, NT and Queensland governments. In the Queensland Water Strategy, for example, the Queensland Government (2023) looks to enable regional economic prosperity through a vision that states ‘Sustainable and secure water resources are central to Queensland’s economic transformation and the legacy we pass on to future generations.’ Acknowledging the need for continued research, the NT Government (2023) announced a Territory Water Plan priority action to accelerate the existing water science program ‘to support best practice water resource management and sustainable development.’ Governments are actively seeking to diversify regional economies, considering a range of factors, including Australia’s energy transformation. The Queensland Government’s economic diversification strategy for North West Queensland (Department of State Development, Manufacturing, Infrastructure and Planning, 2019) includes mining and mineral processing; beef cattle production, cropping and commercial fishing; tourism with an outback focus; and small business, supply chains and emerging industry sectors. In its 2024–25 Budget, the Australian Government announced large investment in renewable hydrogen, low-carbon liquid fuels, critical minerals processing and clean energy processing (Budget Strategy and Outlook, 2024). This includes investing in regions that have ‘traditionally powered Australia’ – as the North West Minerals Province, situated mostly within the Southern Gulf catchments, has done. For very remote areas like the Southern Gulf catchments (Preface Figure 1-1), the land, water and other environmental resources or assets will be key in determining how sustainable regional development might occur. Primary questions in any consideration of sustainable regional development relate to the nature and the scale of opportunities, and their risks. How people perceive those risks is critical, especially in the context of areas such as the Southern Gulf catchments, where approximately 27% of the population is Indigenous (compared to 3.2% for Australia as a whole) and where many Indigenous Peoples still live on the same lands they have inhabited for tens of thousands of years. About 12% of the Southern Gulf catchments are owned by Indigenous Peoples as inalienable freehold. Access to reliable information about resources enables informed discussion and good decision making. Such information includes the amount and type of a resource or asset, where it is found (including in relation to complementary resources), what commercial uses it might have, how the resource changes within a year and across years, the underlying socio-economic context and the possible impacts of development. Most of northern Australia’s land and water resources have not been mapped in sufficient detail to provide the level of information required for reliable resource allocation, to mitigate investment or environmental risks, or to build policy settings that can support good judgments. The Southern Gulf Water Resource Assessment aims to partly address this gap by providing data to better inform decisions on private investment and government expenditure, to account for intersections between existing and potential resource users, and to ensure that net development benefits are maximised. Preface Figure 1-1 Map of Australia showing Assessment area (Southern Gulf catchments) and other recent CSIRO Assessments FGARA = Flinders and Gilbert Agricultural Resource Assessment; NAWRA = Northern Australia Water Resource Assessment. The Assessment differs somewhat from many resource assessments in that it considers a wide range of resources or assets, rather than being a single mapping exercises of, say, soils. It provides a lot of contextual information about the socio-economic profile of the catchments, and the economic possibilities and environmental impacts of development. Further, it considers many of the different resource and asset types in an integrated way, rather than separately. The Assessment has agricultural developments as its primary focus, but it also considers opportunities for and intersections between other types of water-dependent development. For example, the Assessment explores the nature, scale, location and impacts of developments relating to industrial, urban and aquaculture development, in relevant locations. The outcome of no change in land use or water resource development is also valid. The Assessment was designed to inform consideration of development, not to enable any particular development to occur. As such, the Assessment informs – but does not seek to replace – existing planning, regulatory or approval processes. Importantly, the Assessment does not assume a given policy or regulatory environment. Policy and regulations can change, so this flexibility enables the results to be applied to the widest range of uses for the longest possible time frame. It was not the intention of – and nor was it possible for – the Assessment to generate new information on all topics related to water and irrigation development in northern Australia. Topics P155#yIS1 not directly examined in the Assessment are discussed with reference to and in the context of the existing literature. CSIRO has strong organisational commitments to Indigenous reconciliation and to conducting ethical research with the free, prior and informed consent of human participants. The Assessment allocated significant time to consulting with Indigenous representative organisations and Traditional Owner groups from the catchments to aid their understanding and potential engagement with its requirements. The Assessment did not conduct significant fieldwork without the consent of Traditional Owners. CSIRO met the requirement to create new scientific knowledge about the catchments (e.g. on land suitability) by synthesising new material from existing information, complemented by remotely sensed data and numerical modelling. Functionally, the Assessment adopted an activities-based approach (reflected in the content and structure of the outputs and products), comprising activity groups, each contributing its part to create a cohesive picture of regional development opportunities, costs and benefits, but also risks. Preface Figure 1-2 illustrates the high-level links between the activities and the general flow of information in the Assessment. Preface Figure 1-2 Schematic of the high-level linkages between the eight activity groups and the general flow of information in the Assessment Assessment reporting structure Development opportunities and their impacts are frequently highly interdependent and, consequently, so is the research undertaken through this Assessment. While each report may be read as a stand-alone document, the suite of reports for each Assessment most reliably informs discussion and decisions concerning regional development when read as a whole. P164#yIS1 The Assessment has produced a series of cascading reports and information products: • Technical reports present scientific work with sufficient detail for technical and scientific experts to reproduce the work. Each of the activities (Preface Figure 1-2) has one or more corresponding technical reports. • A catchment report, which synthesises key material from the technical reports, providing well- informed (but not necessarily scientifically trained) users with the information required to inform decisions about the opportunities, costs and benefits, but also risks, associated with irrigated agriculture and other development options. • A summary report provides a shorter summary and narrative for a general public audience in plain English. • A summary fact sheet provides key findings for a general public audience in the shortest possible format. The Assessment has also developed online information products to enable users to better access information that is not readily available in print format. All of these reports, information tools and data products are available online at https://www.csiro.au/southerngulf. The webpages give users access to a communications suite including fact sheets, multimedia content, FAQs, reports and links to related sites, particularly about other research in northern Australia. Executive summary The freshwater, terrestrial and near-shore marine zones of the Southern Gulf catchments contain important and diverse species, habitats, industries and ecosystem functions supported by the patterns and volumes of river flow. Although irrigated agriculture may only occupy a small percentage of the landscape, changes in the flow regime can have profound effects on flow- dependent flora and fauna and their habitats, and these changes may extend considerable distances onto the floodplain and downstream, including into the marine environment. To understand how ecological flow dependencies of ‘ecological assets’ (species, species groups and habitats that have dependencies on freshwater flows) could change with potential water resource development a wide range of hypothetical development scenarios were explored. Important attributes of the flow regime for the ecological assets were identified and the change in these assessed as either negligible, minor, moderate, major or extreme given the historical range of these metrics at each assessment location. Simulations of water harvesting and instream dams showed varied levels of ecological impact depending on the resulting flow volumes and patterns. • The level of change in important flows for ecology resulting from water resource development was highly dependent on the type of development, the extraction volume and the mitigation measures implemented given the location of assets across the catchment, and the flow volumes occurring within the river reach. • For different development scenarios with equivalent extractions (i.e. dam development and water harvesting that result in similar changes in flow volume), instream dam development typically resulted in a higher mean change for ecology metrics such as high-flow volumes or duration of low flows, averaged across the catchment, compared to roughly equivalent water harvesting scenarios, especially in the absence of significant mitigation measures. The more dams, the larger the relative difference compared to water harvesting. For water harvesting, volume matters, but impacts of flow dependency of water dependent ecological assets can be considerably offset with measures (e.g. regulation) that mitigate the impact of water harvesting. • Water harvesting without any mitigation measures (which is unlikely to occur in reality) resulted in mean changes assessed as minor to assets across the Southern Gulf catchments for extraction targets from 50 to 300 GL annually with changes in flow often accumulating downstream past multiple extraction points. • Mud crabs, threadfin, prawn species and mullet were among the ecological assets most affected by flow change for water harvesting. • For equivalent extraction volumes, providing suitable levels of annual diversion commencement flow requirements, commence-to-pump thresholds and pump rates improved ecology outcomes to negligible at catchment scales. This demonstrates the importance of protecting minimum flows and first flows for many of the ecological assets. For potential instream dams, location matters, with potential for high risks of local impacts. Improved outcomes are associated with maintaining attributes of the natural flow regime. • Potential dams on the Gregory River and Gunpowder Creek resulted in minor and negligible mean change to assets flows at the catchment scale, respectively. Those two dams combined resulted in minor change at the catchment scale. • Local impacts were highest directly downstream of dams, with often extreme changes in flow dependencies for assets including inchannel waterholes, cryptic wading waterbirds, sawfish and grunter, all of which require low or stable flows. However, areas further downstream of potential dams experienced less change due to contributions from unimpacted tributaries. • Mangroves, mullet, cryptic wader birds, prawns and mud crabs were among the ecological assets most affected by instream dams. • Providing transparent flows—allowing inflows to pass the dam wall for environmental purposes—provided improvements for most assets compared to without these, particularly for mullet, threadfin, prawn species and mangroves (major to moderate mean changes). But it is not just flow; other impacts and considerations are also important. • Climate change had larger potential mean change in flows across catchments than most water resource development scenarios. Under a drying climate, flow regime change resulted in a moderate change in important flow dependencies across all assets, exceeding the effects of feasible dam or water harvesting scenarios. • The combination of a dry climate and water resource development compounded the pressures on ecological systems, resulting in the most significant catchment-level changes and moderate alterations to asset flow dependencies. Contents Director’s foreword .......................................................................................................................... i The Southern Gulf Water Resource Assessment Team .................................................................. ii Shortened forms .............................................................................................................................iii Units ............................................................................................................................... iv Preface ............................................................................................................................... v Executive summary ......................................................................................................................... ix 1 Introduction ........................................................................................................................ 1 1.1 Water resource development and flow ecology ................................................... 1 1.2 Ecology of the Southern Gulf catchments ............................................................. 2 2 Methods .............................................................................................................................. 5 2.1 Scenarios of water resource development and future climate ............................ 5 2.2 Ecological modelling and the analysis approach ................................................. 12 3 Catchment results and implications ................................................................................. 21 3.1 Water resource development scenario result overviews ................................... 21 4 Asset assessments ............................................................................................................ 34 4.1 Fish, sharks and rays ............................................................................................ 34 4.2 Waterbirds ........................................................................................................... 66 4.3 Prawns, turtles and other species ....................................................................... 90 4.4 Freshwater-dependent habitats ........................................................................ 110 5 Synthesis ......................................................................................................................... 145 References ........................................................................................................................... 150 Assessment nodes for ecological assets and habitat suitability weighting ...... 171 Mean changes in catchment flow for specific species and habitats for selected assets across different water harvesting scenarios .................................................................... 179 Asset hydrometrics and their weightings in flow dependencies modelling ..... 184 Asset metrics with the largest contribution to changes in asset flow dependencies by scenario ........................................................................................................... 192 Waterbird groups and their species .................................................................. 216 Figures Preface Figure 1-1 Map of Australia showing Assessment area (Southern Gulf catchments) and other recent CSIRO Assessments .................................................................................................... vi Preface Figure 1-2 Schematic of the high-level linkages between the eight activity groups and the general flow of information in the Assessment ...................................................................... vii Figure 2-1 Locations of the river system modelling nodes at which flow–ecology relationships were assessed (numbered) and the locations of hypothetical developments .............................. 7 Figure 2-2 Flow dependencies analysis conceptual models for linking flow–ecology relationships for different assets to important parts of the flow regime under hypothetical wet, medium and dry years ........................................................................................................................................ 14 Figure 2-3 Considering scenario change based upon the percentile change from the distribution of each metric under the historical flow at each node ................................................................. 15 Figure 2-4 Spatial weighting of the metrics for the freshwater-dependent ecological assets in the Southern Gulf catchments ...................................................................................................... 15 Figure 2-5 Locations of the floodplain wetlands used in the lateral connectivity analysis .......... 19 Figure 3-1 Spatial heatmap of change in asset flow dependencies across the Southern Gulf catchments considering change across all assets in the locations which each asset is assessed 23 Figure 3-2 Mean change to important flow dependencies for assets across scenarios and nodes ....................................................................................................................................................... 24 Figure 3-3 Percentage of nodes with different changes in important asset flow dependencies aggregated for all assets ............................................................................................................... 25 Figure 3-4 Mean change associated with each asset’s important metrics across water harvesting increments of system target and pump-start threshold with no annual diversion commencement flow requirement and pump rate of 30 days .................................................... 26 Figure 3-5 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and pump-start threshold for an annual diversion commencement flow requirement of 250 GL and pump rate of 30 days .................................... 27 Figure 3-6 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and end-of-system (EOS) requirement with a pump threshold of 200 ML/day and a pump rate of 30 days ....................................................... 29 Figure 3-7 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and pump rate with a pump threshold of 200ML/day and a pump rate of 30 days and no annual diversion commencement flow requirement .................................................................................................................................. 31 Figure 4-1 Spatial heatmap of habitat-weighted changes in flow for barramundi, considering the assets important locations across the catchment ........................................................................ 36 Figure 4-2 Habitat-weighted change in barramundi flow dependencies by scenario across model nodes ............................................................................................................................................. 38 Figure 4-3 Spatial heatmap of habitat-weighted changes in flow for catfish, considering the assets important locations across the catchment ........................................................................ 42 Figure 4-4 Habitat-weighted change in catfish flow dependencies by scenario across model nodes ............................................................................................................................................. 44 Figure 4-5 Spatial heatmap of habitat-weighted changes in flow for grunters, considering the assets important locations across the catchment ........................................................................ 48 Figure 4-6 Habitat-weighted change in grunter flow dependencies by scenario across model nodes ............................................................................................................................................. 50 Figure 4-7 Change in mullet flow dependencies by scenario for the end-of-system node ......... 54 Figure 4-8 Spatial heatmap of habitat-weighted changes in flow for sawfish, considering the assets important locations across the catchment ........................................................................ 58 Figure 4-9 Habitat-weighted change in sawfish flow dependencies by scenario across model nodes ............................................................................................................................................. 60 Figure 4-10 Change in threadfin flow dependencies by scenario for the end-of-system node ... 64 Figure 4-11 Spatial heatmap of habitat-weighted changes in flow for colonial and semi-colonial wading waterbirds, considering the assets important locations across the catchment .............. 68 Figure 4-12 Habitat-weighted change in colonial and semi-colonial wading waterbird flow dependencies by scenario across model nodes ........................................................................... 70 Figure 4-13 Spatial heatmap of habitat-weighted changes in flow for cryptic wading waterbirds, considering the assets important locations across the catchment .............................................. 74 Figure 4-14 Habitat-weighted change in cryptic wading waterbird flow dependencies by scenario across model nodes ........................................................................................................ 76 Figure 4-15 Spatial heatmap of habitat-weighted changes in flow for shorebirds, considering the assets important locations across the catchment ........................................................................ 80 Figure 4-16 Habitat-weighted change in shorebird flow dependencies by scenario across model nodes ............................................................................................................................................. 82 Figure 4-17 Spatial heatmap of habitat-weighted changes in flow for swimming, diving and grazing waterbirds, considering the assets important locations across the catchment .............. 86 Figure 4-18 Habitat-weighted change in swimming, diving and grazing waterbirds flow dependencies by scenario across model nodes ........................................................................... 88 Figure 4-19 Change in banana prawn flow dependencies by scenario for the end-of-system node .............................................................................................................................................. 92 Figure 4-20 Change in Endeavour prawn flow dependencies by scenario for the end-of-system node .............................................................................................................................................. 96 Figure 4-21 Change in tiger prawns flow dependencies by scenario for the end-of-system node ....................................................................................................................................................... 99 Figure 4-22 Spatial heatmap of habitat-weighted changes in flow for freshwater turtles, considering the assets important locations across the catchment ............................................ 102 Figure 4-23 Habitat-weighted change in freshwater turtles flow dependencies by scenario across model nodes .................................................................................................................... 104 Figure 4-24 Change in mud crabs flow dependencies by scenario for the end-of-system node 108 Figure 4-25 Spatial heatmap of habitat-weighted changes in flow for floodplain wetlands, considering the assets important locations across the catchment ............................................ 112 Figure 4-26 Habitat-weighted change in floodplain wetlands flow dependencies by scenario across model nodes .................................................................................................................... 114 Figure 4-27 Time series of the floodplain inundation for each scenario for the 2016 modelled flood event in the Southern Gulf catchments ............................................................................ 118 Figure 4-28 Time series of the floodplain inundation for each scenario for the 2023 modelled flood event in the Southern Gulf catchments ............................................................................ 118 Figure 4-29 Maximum floodplain inundation for each scenario for the 2016 modelled flood event in the Southern Gulf catchments ...................................................................................... 120 Figure 4-30 Maximum floodplain inundation for each scenario for the 2023 flood modelled event in the Southern Gulf catchments ...................................................................................... 121 Figure 4-31 Spatial heatmap of habitat-weighted changes in flow for inchannel waterholes, considering the assets important locations across the catchment ............................................ 123 Figure 4-32 Habitat-weighted change in inchannel waterhole flow dependencies by scenario across model nodes .................................................................................................................... 125 Figure 4-33 Change in mangroves flow dependencies by scenario for the end-of-system node ..................................................................................................................................................... 129 Figure 4-34 Change in saltpans and salt flats flow dependencies by scenario for the end-of- system node ................................................................................................................................ 133 Figure 4-35 Change in seagrasses flow dependencies by scenario for the end-of-system node ..................................................................................................................................................... 136 Figure 4-36 Spatial heatmap of habitat-weighted changes in flow for surface-water-dependent vegetation, considering the assets important locations across the catchment ......................... 140 Figure 4-37 Habitat-weighted change in surface-water-dependent vegetation flow dependencies by scenario across model nodes ......................................................................... 142 Tables Table 2-1 Water resource development and climate scenarios explored in this ecology analysis††† ........................................................................................................................................ 9 Table 2-2 Ecological assets groups, along with the individual assets, and their associated systems, with the relevant sections of the report ........................................................................ 13 Table 2-3 Reporting values for the flow dependencies modelling as rank percentile change of the hydrometrics, considering the change in mean metric value against the distribution observed under Scenario AE ......................................................................................................... 16 Table 2-4 Water resource development and climate scenarios explored in this ecology analysis ....................................................................................................................................................... 20 Table 3-1 Scenarios of different hypothetical instream dam locations showing mean changes of ecology flows for assets groups, assessed at their respective catchment nodes ........................ 33 Table 4-1 Maximum floodplain inundation (in km2) and percentage change from Scenario AE as the maximum flood extent for each scenario for 2016 modelled flood event in the Southern Gulf catchments .......................................................................................................................... 117 Table 4-2 Maximum floodplain inundation (in km2) and percentage change from Scenario AE as the maximum flood extent for each scenario for a 2023 modelled flood event in the Southern Gulf catchments .......................................................................................................................... 117 1 Introduction 1.1 Water resource development and flow ecology The ecology of a river is intricately linked to its flow regime, and species are broadly adapted to the prevailing conditions under which they occur. These ecological relationships within freshwater systems are not only affected by the persistence or ephemerality of rivers. They are also influenced by the volume of river flows and patterns of floodplain inundation and discharges that support species, habitats and ecosystem functions. Flow-dependent flora, fauna and habitats are defined here as those sensitive to changes in flow and those sustained by either surface water or groundwater flows or a combination of these. In rivers and floodplains, the capture, storage, release, conveyancing and extraction of water alters the environmental template on which the river functions, and water regulation is frequently considered one of the biggest threats to aquatic ecosystems worldwide (Bunn and Arthington, 2002; Poff et al., 2007). Changes in flows due to water resource development can act on both wet and dry periods to change the magnitude, timing, duration and frequency of flows (Jardine et al., 2015; McMahon and Finlayson, 2003). Impacts on fauna, flora and habitats associated with flow regime change can often extend considerable distances downstream from the source of impact and into near-shore coastal and marine areas as well as onto floodplains (Burford et al., 2011; Nielsen et al., 2020; Pollino et al., 2018). Although the science has become increasingly better understood, there remains an inherent complexity associated with understanding the environmental risks associated with water resource development, and particularly in northern Australia. This is in part because of the diversity of species and habitats distributed across and within the catchments and the near-shore marine zones, and because water resource development can produce a broad range of direct and indirect environmental impacts. These impacts can include changes to flow regime, loss of habitat, loss of function such as connectivity, changes to water quality, and the establishment of pest species. Instream dams create large bodies of standing water that inundate terrestrial habitat and result in the loss of the original stream and riverine conditions (Nilsson and Berggren, 2000; Schmutz and Sendzimir, 2018). Storages can capture flood pulses and reduce the volume and extent of water that transports important nutrients into estuaries and coastal waters via flood plumes (Burford et al., 2016; Burford and Faggotter, 2021; Tockner et al., 2010). Further, even minor instream barriers, such as road causeways, can disrupt migration and movement pathways, causing fragmentation of populations and loss of essential habitat for species that need passage along the river (Crook et al., 2015; Pelicice et al., 2015). With water resource development and irrigation comes increased human activity. This can add additional pressures, including biosecurity risks associated with invasive or pest species transferring into new habitats or increasing their advantage in modified habitats (Pyšek et al., 2020). This report analyses the change in important flow metrics resulting from changes associated with a variety of types, scales and locations of hypothetical water resource development in the Southern Gulf catchments to flow-dependent freshwater, estuarine and near-shore marine assets, and terrestrial systems. See the companion technical report on water storages, (Yang et al., 2024), for more details on the impacts of habitat loss within potential dam impoundments and connectivity loss due to the development of new instream barriers. Refer to the companion technical report on ecological asset descriptions in the Southern Gulf catchments by Merrin et al. (2024), for a qualitative overview of groundwater-dependent ecosystems in the context of water resource development. Merrin et al. (2024) also qualitatively examines existing and potential threatening processes for fresh-water-dependent ecological assets, including possible influences of synergistic impacts. 1.2 Ecology of the Southern Gulf catchments The Southern Gulf catchments, spanning 108,200 km² across the Northern Territory (NT) and Queensland, encompass several significant protected areas (Merrin et al., 2024). Among these are the UNESCO World Heritage–listed Australian Fossil Mammal Sites (Riversleigh), which are recognised for their rich fossil deposits. The study area also includes three Indigenous Protected Areas (Ganalanga-Mindibirrina, Nijinda Durlga, and Thuwathu/Bujimulla), which play a crucial role in preserving Indigenous cultural heritage and biodiversity. National and conservation parks in this area, such as Boodjamulla and Finucane Island National parks, offer protection to diverse ecosystems. Furthermore, 13 wetlands listed in the Directory of Important Wetlands in Australia (DIWA) are located in the study area, including Bluebush Swamp, Buffalo Lake Aggregation and Lawn Hill Gorge (Department of Agriculture‚ Water and the Environment, 2021a). These wetlands are vital for maintaining regional biodiversity and supporting migratory bird species. The Southern Gulf catchments are notable for their high biodiversity, supporting at least 170 species of fish, 150 species of waterbirds, 30 species of aquatic and semi-aquatic reptiles, 60 species of amphibians, and 100 macroinvertebrate families (van Dam et al., 2008b). The region’s freshwater systems are particularly rich in species adapted to the highly seasonal flow regimes of the wet-dry tropics. Several species in the area are listed as Critically endangered, Endangered, or Vulnerable under the Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) and by state conservation systems. Notable among these are the freshwater or largetooth sawfish (Pristis pristis; Vulnerable), and the Gulf snapping turtle (Elseya lavarackorum; Endangered). The Southern Gulf catchments also serve as crucial stopover habitats for migratory shorebird species like the eastern curlew (Numenius madagascariensis; Critically endangered), and the Australian painted snipe (Rostratula australis; Endangered) (Department of Agriculture‚ Water and the Environment, 2021b). The Southern Gulf catchments encompass rich marine and estuarine environments that are vital for both commercial and ecological purposes (see Merrin et al. (2024)). These environments feature extensive intertidal flats and estuarine communities, including mangroves, salt flats, and seagrass habitats. These habitats are highly productive, have high culturally value and include many areas of national importance (Poiner et al., 1987). The seagrass beds in the region are highly diverse and productive, providing essential food and habitat for dugongs (Dugong dugon), green turtles (Chelonia mydas), and prawns (Loneragan et al., 1997; Poiner et al., 1987). These ecosystems are also vital for a major commercial barramundi fishery (Bayliss et al., 2014), which is economically significant for the region. Additionally, mud crabs (mainly Scylla serrata) are harvested in these waters (Bayliss et al., 2014). The area’s estuarine and near-coastal habitats, including extensive intertidal flats and mangroves, support a variety of species and are of high cultural and national significance. Groundwater dependent ecosystems play a crucial role in the Southern Gulf catchments, encompassing aquatic, terrestrial, and subterranean habitats. Aquatic groundwater-dependent ecosystems, including springs and river sections that retain water throughout the dry season, are vital for supporting aquatic species and fringing vegetation (James et al., 2013). Terrestrial groundwater-dependent ecosystems rely on water sources such as direct rainfall, bank recharge, floodplain inundation, and shallow groundwater. These ecosystems support diverse species and provide critical habitats, especially during the dry season when surface water is scarce. Subterranean aquatic ecosystems, particularly in limestone areas, harbour unique fauna that depend on the presence of groundwater (Hose et al., 2015). The hydrology of the Southern Gulf catchments is driven by a wet-dry tropical climate, characterised by an extended dry season and a wet season during which most of the rainfall occurs. The mean annual rainfall averaged across the study area is approximately 602 mm of which 94% falls during the wet season (see companion technical report on climate, McJannet et al. (2023)). This seasonal rainfall, along with high potential evapotranspiration and groundwater discharge, shapes the flow regimes in the catchment’s rivers and streams. The region’s dominant vegetation types include open eucalypt woodlands, melaleuca forests and tussock grasslands (Department of Climate Change‚ Energy‚ the Environment and Water, 2020), which are adapted to the variable water availability. Groundwater-fed perennial rivers, such as the Gregory and O’Shannassy rivers and Lawn Hill Creek, along with creeks, permanent lakes and in-channel waterholes, provide essential refuge habitats for a diverse array of aquatic species during the dry season in this semi-arid environment (McJannet et al., 2014; Waltham et al., 2013). These water sources are crucial for species survival and enable recolonisation into surrounding habitats when larger flows return (Hermoso et al., 2013). During the wet season (1 November to 30 April), widespread flooding can occur in the Southern Gulf catchments, inundating floodplains and connecting wetlands to the river channels. This process enhances primary and secondary productivity by facilitating the exchange of nutrients and organic matter between aquatic and terrestrial ecosystems (Pettit et al., 2011). Flooding is particularly notable in the lower parts of the study area, including the floodplain wetlands and extensive intertidal flats along the mainland coastline south of Bentinck and Sweers islands. These floods discharge into the marine waters and support high levels of marine productivity, which in turn sustains important fisheries and ecological processes (Burford et al., 2016; Burford and Faggotter, 2021; Leahy and Robins, 2021; Ndehedehe et al., 2020; Ndehedehe et al., 2021). This report builds upon, and should be considered in conjunction with, the descriptions of the ecological assets in Merrin et al. (2024). It seeks to address the question: What is the relative risk to ecology associated with potential water resource development in the Southern Gulf catchments? To address this question, the report is structured as follows: • Section 2 provides details of the hypothetical scenarios and quantitative methods used to understand the relative change in important flows associated with water resource development in the Southern Gulf catchments for selected ecological assets. • Section 3 provides a high-level overview of the scenarios, showing aggregated results (mean of assets in broad asset groups), and discusses specific differences in the spatial pattern and magnitude of change between scenarios. These differences include different potential water resource development options and their mitigation and management. • Section 4 provides an overview and discussion of the modelling results for the selected ecological assets across a subset of scenarios. Outcomes for particular ecological assets consider their water needs, distribution within the catchment and the range of flow conditions occurring under each of the scenarios using a range of different methods. The ecological context of the change in flows are discussed for each asset. • Section 5 provides a synthesis of outcomes from the ecology assessment in the context of the Southern Gulf catchments. The ecology of the Southern Gulf catchments, including the knowledge base for selected ecological assets and their flow ecology is further detailed in Merrin et al. (2024). This companion technical report should be consulted for additional information with regards to the ecology of the catchment, the assets and their flow-relationships. 2 Methods This ecology analysis aims to assess the relative risks to species and habitats posed by potential water resource development in the Southern Gulf catchments. The goal is to support long-term decision-making and planning processes for sustainable and responsible development in northern Australia. The potential development scenarios presented here are hypothetical and serve to explore a range of options and issues associated with water resource development in the Southern Gulf catchments. In the event of any future development occurring, additional studies would need to evaluate the environmental impacts associated with the specific development across a broad range of environmental considerations, including water quality. Note that this ecology analysis is broad in scale and includes significant uncertainty in results. This uncertainty is due to a range of factors, including incomplete knowledge, variability within and between catchments, and limitations in data and modelling processes. Furthermore, the modelling process may not have adequately captured unknown thresholds, temporal processes, issues of scale or local conditions, ecological interactions, synergistic effects and feedback responses in the ecology of the system. There is also uncertainty associated with possible future weather and climate conditions, such as rainfall patterns, and any additional synergistic threatening processes that may emerge. Northern Australia is vast and diverse, and the knowledge base of species occurrences is limited. More broadly, the understanding of freshwater ecology in northern Australia is still developing. 2.1 Scenarios of water resource development and future climate This ecology analysis used the outputs of river models for the Nicholson-Gregory and Leichhardt catchments to explore the potential impacts of hypothetical water resource development in the Southern Gulf catchments. The river model setup and calibration process are described in the companion technical report on river system modelling in the Southern Gulf catchments by Gibbs et al. (2024a) and the river model scenario simulations are outlined in the companion hydrology simulation report (Gibbs et al., 2024b). The scenarios were selected to explore how different types, locations and scales of water resource development might impact important flow dependencies of water-dependent ecosystems found in the study area. Changes in water quality may also affect ecology but are not considered in the quantitative analysis. The hypothetical water resource developments considered include instream infrastructure (large dams) and water harvesting (pumping river water into off-stream farm-scale storages). Consult Section 1.2.2 of the Southern Gulf catchments report (Watson et al., 2024) when evaluating the likelihood of a hypothetical development scenario occurring. Scenarios also explored the effects of a dry future climate on runoff and water-dependent ecosystems, as well as the interactions between potential water resource development and the dry climate future. The scenario terminology used in the Assessment is broadly described in Table 2-1. Gibbs et al. (2024b) provides further details of the river system modelling and scenarios. The hydrology generated with the Southern Gulf AWRA-R model (Gibbs et al., 2024a) included processes for considering rainfall, evaporation and runoff, routing of water across subcatchments, losses, irrigation extraction and reservoir behaviour. These parameters were modelled across 77 nodes within the Southern Gulf catchments (nodes and hypothetical development locations are shown in Figure 2-1). A long time series of daily flow from 1 September 1890 to 31 August 2022 was generated (132-year climate series) and used except where otherwise stated. This period provided a wide range of environmental conditions that encompassed extended dry periods, including those that occurred in the first half of the 20th century and periods of variability, including both low-flow and high-flow conditions across scales of days and inter-decadal variability, and different event sequencing. 2.1.1 Key terminology used in this report Water harvesting – an operation where water is pumped or diverted from a river into an offstream storage. Offstream storages– usually fully enclosed, circular or rectangular earthfill embankment structures situated close to major watercourses or rivers so as to minimise the cost of pumping. Large engineered instream dams – usually constructed from earth, rock or concrete materials as a barrier across a river to store water in the reservoir created and intercept a drainage line (Yang et al., 2024). Annual diversion commencement flow requirement (DCFR) – The cumulative volume passing the most downstream nodes in catchments with water harvest (on the Leichhardt River node 9130071, Albert River node 9129040 and Nicholson River node 9121090) from the start of the water year required before water harvest pumping can commence. Pump-start threshold – a daily flow rate threshold above which pumping or diversion of water can commence. This is usually implemented as a strategy to minimise the ecological impact of water harvesting. Pump capacity – the capacity of the pumps expressed as the number of days it would take to pump the entire node irrigation target. Reach irrigation volumetric target – the maximum volume of water extracted in a river reach over a water year. Note: the end use is not necessarily limited to irrigation. Users could also be involved in aquaculture, mining, urban or industrial activities. System irrigation volumetric target – the maximum volume of water extracted across the entire study area over a water year. Note: the end use is not necessarily limited to irrigation. Users could also be involved in aquaculture, mining, urban or industrial activities. Transparent flow – a strategy to mitigate the ecological impacts of large instream dams by allowing all reservoir inflows below a flow threshold to pass ‘through’ the dam. Figure 2-1 Locations of the river system modelling nodes at which flow–ecology relationships were assessed (numbered) and the locations of hypothetical developments The flow ecology of the environmental assets was assessed in subcatchments downstream of the river system nodes. The locations of assets across the catchment are documented in Merrin et al. (2024), with the assessment nodes for each asset provided in the corresponding sections of this report and their habitat suitability weightings compiled in Appendix A. A combined EOS node (9100000) was used for marine assets, which combined flows from the Nicholson (9121090 and 9129040) and the Leichhardt (9130071). P316#yIS1 2.1.2 Scenario definitions The Assessment considered four scenarios with subsets, reflecting combinations of different levels of development and historical and future climates: • Scenario A – historical climate and no hypothetical development • Scenario B – historical climate and hypothetical future development • Scenario C – future climate and current level of development • Scenario D – future climate and hypothetical future development. Further details are provided in Table 2-1 showing the subsets of each scenario. Scenario A – historical climate and no hypothetical development Scenario A and its subsets, Scenario A Existing (AE) and Scenario A Natural (AN), all assumed a historical climate (Table 2-1). The historical climate series was defined as the observed rainfall, temperature and potential evaporation for water years from 1 September 1890 to 31 August 2022. All results presented in this report were calculated over this period unless specified otherwise. Scenario AE represents the current levels of development and current use of existing entitlements, which are underutilised in the Southern Gulf catchments (DRDMW, 2023). Scenario AE can be considered as representative of current streamflow characteristics in the study area. Scenario AE is the baseline to which change to important ecology flow dependencies is compared. Scenario A assumes historical climate and full use of existing entitlements. In contrast, Scenario AN assumes historical climate and assumes levels of water resource development as existed prior to European development (considered to be ‘natural’ flow scenario). Scenario B – historical climate and hypothetical future development Scenario B explores a historical climate with hypothetical future development and full use of existing entitlements (with water use equivalent to Scenario A). Using the same historical climate data as Scenario A, the model is adjusted to assess the impacts of potential future development on hydrological, ecological, and economic systems (Table 2-1). Scenario B considers two types of water resource development: increased water harvest extraction directly from watercourses, evaluated under various scales and operating conditions such as pump capacity, flow thresholds, and seasonal commencement requirements; and the construction of large instream dams, with trade-offs analysed between storing inflows and providing transparent flows through controlled releases. Scenario C – future climate and current level of development Scenario C projects future climate conditions around 2060, assuming full use of existing surface water and groundwater entitlements as under Scenario A (Table 2-1). This scenario is based on a 132-year climate series derived from Global Climate Model (GCM) projections, reflecting an approximate 1.6 C global temperature rise by 2060 compared to 1990. This projection aligns with the Shared Socioeconomic Pathway SSP2-4.5 from the IPCC Sixth Assessment Report (IPCC, 2022). SSP2-4.5 represents a ‘middle-of-the-road’ scenario where global development continues at a moderate pace, and mitigation efforts lead to moderate greenhouse gas emissions, with an expected global temperature rise of around 2.7°C by 2100. The Shared Socioeconomic Pathways (SSPs) are scenarios developed by the IPCC to explore how different levels of societal development and policy choices might influence future climate outcomes. Three potential climate futures were modelled in Gibbs et al. (2024b). Scenarios CEwet, CEmid and CEdry represented the 10th, 50th and 90th percent exceedance of mean annual rainfall, spatially averaged across the Southern Gulf catchments from the 32 GCM projections examined. The ecology activity explored changes only under CEdry, being the most conservative scenario and under which competition for water would be greatest (Table 2-1). See companion technical report on climate, McJannet et al. (2023), for more detail. Scenario D – future climate and hypothetical future development Scenario D assessed future climate and hypothetical future development (in addition to current levels of development of water). It used the same future climate series as Scenario CEdry. River inflow were modified to reflect hypothetical development, as in Scenario B (Table 2-1). Therefore, in this report, the climate data under scenarios A and B were the same (historical observations from 1 September 1890 to 1 August 2022) and the climate data under scenarios C and D were the same (the above historical data scaled to reflect a plausible range of future climates). Table 2-1 Water resource development and climate scenarios explored in this ecology analysis††† Gibbs et al. (2024a) and Gibbs et al. (2024b) describes the river system modelling and additional scenario details. SCENARIO DESCRIPTION ASSUMES FULL USE OF EXISTING LICENCES TRANSPARENT FLOW THRESHOLD (% OF MEAN INFLOW) TARGET EXTRACTION VOLUME (GL) ANNUAL DIVERSION COMMENCEMENT FLOW REQUIREMENT (GL) PUMP-START THRESHOLD (ML/D) PUMP CAPACITY (D) Scenario A Historical climate and no hypothetical development AN No development – natural conditions No (no use) na 0 na na na AE Current (2023) levels of development No (current) (29.9 GL) No 0 0 variable variable A Full use of existing entitlements Yes (113.5 GL) No 0 0 variable variable Scenario B Historical climate and hypothetical future development B-DGPC B-DGR Single hypothetical dams‡ Yes (113.5 GL) No na‡ na na na B-D2 Two hypothetical dams together (B-DGPC and B- DGR) Yes (113.5 GL) No na‡ na na na B-DGPCT B-DGRT Single hypothetical dams‡ with transparent flows Yes (113.5 GL) Q = 20 na‡ na na na SCENARIO DESCRIPTION ASSUMES FULL USE OF EXISTING LICENCES TRANSPARENT FLOW THRESHOLD (% OF MEAN INFLOW) TARGET EXTRACTION VOLUME (GL) ANNUAL DIVERSION COMMENCEMENT FLOW REQUIREMENT (GL) PUMP-START THRESHOLD (ML/D) PUMP CAPACITY (D) B-D2T Two hypothetical dams together with transparent flows (B- DGPCT and B-DGRT) Yes (113.5 GL) Q = 20 na‡ na na na B-WT150P600R30E0 Water harvesting varying target extraction volume (T), annual diversion commencement flow requirement (E), pump- start threshold (P), and/or pump capacities (R) Yes (113.5 GL) na T = 50, 150, 300… E = 0, 150… P = 200, 600… R = 10, 20… Scenario C Future climate and current level of development CEdry Dry (10th percentile exceedance) GCM†† projection No (current) (29.9GL) No 113.5† 0 variable variable Scenario D Future climate and hypothetical future development Ddry-DGPC Single hypothetical dams‡ with Scenario CEdry Yes (113.5 GL) No na‡ na na na Ddry-D2 Two hypothetical dams (same as B-D2) with Scenario CEdry Yes (113.5 GL) No na‡ na na na Ddry-D2T Two hypothetical dams (same as B-D2) with Scenario CEdry with transparent flows Yes (113.5 GL) Q = 20 na‡ na na na Ddry- WT150P600R30E0 Water harvesting with Scenario CEdry Yes (113.5 GL) na T = 50, 150, 300… E = 0, 150, 250… P = 200, 600… R = 30… ‡No target volume for hypothetical dam scenarios; instead, a target extraction volume that could be met with 85% reliability was identified. ††GCM = global climate model. †††The scenarios used in the hydrodynamic modelling used in the lateral connectivity analysis are described in Section 2.2.2. na = not applicable The ecology analysis considered ecological change from Scenario AE as a reference point, acknowledging that the current conditions, which have persisted for several decades, and is assumed that water-dependent-assets have reached a pseudo-equilibrium with new flow regime. A complementary ecological analysis used inputs from a hydrodynamic model, which provided estimates of flood extent, water depth and velocity for sample flood events of different magnitudes and durations. See the companion technical report on flood modelling, Karim et al. (2024), for more detail. The different potential water resource development pathways resulted in different changes to flow regimes, considering rainfall and upstream catchment sizes, inflows, the attenuation of flow through the river system (including accumulating inflows with river confluences), and the many ways each water resource development could unfold and be implemented and managed. The scenarios provided in Table 2-1 were used to explore some of the interactions between the location and the types and scale of development and their potential mitigation, and how these may influence ecological outcomes within and across the catchment (see sections 3 and 4 for results). For marine assets, a combined EOS node (9100000) was used which combined flows from the Nicholson (9121090 and 9129040) and the Leichhardt (9130071) rivers. Many of the hypothetical scenarios listed in Table 2-1 do not provide minimum level of flows for the environment (for dams, transparent flows and for water harvesting actions such as pump-start thresholds and annual diversion commencement flow requirements). They were optimised for water yield reliability deliberately not considering policy settings or additional restrictions that may mitigate the impacts on water-dependent ecosystems. These scenarios are useful for assessing the level in change of ecologically important flows of different development options in the absence of mitigation measures or policy settings and are conservate as they effectively represent a situation where there was no regulatory compliance. By comparing these scenarios to those that incorporate different mitigation strategies including transparent flows or different annual diversion commencement flow requirements, it becomes possible to identify the relative benefits of various mitigation options to important asset flow dependencies. The river system model structure assumed that water was extracted directly ‘at the dam wall’ rather than conveyed downstream for irrigation within the river channel. Typically, for the purpose of agricultural use, water is released down the river channel and pumped from the water in the river behind a re-regulating structure near the demand. However, for this analysis, a conservative option was adopted, and water was extracted directly from the potential dam reservoir and piped to the demand. This option has higher impacts on changes to flows because water is extracted from the river sooner and without the change in flow being ‘dampened’ by tributary inflows downstream of the potential dam. Hence, for the hypothetical dam scenarios, the modelled flow volumes directly downstream of the dam are likely lower than what would be expected in a real-world dam setting where water is conveyed to downstream users using the river. In a real-world setting, management and regulatory requirements would likely provide a range of greater safeguards for environmental outcomes, possibly establishing a combination of transparent flows, annual diversion commencement flow requirement, extraction limits and minimum flow or pump-start thresholds (see Section 2.1.1). Each of these safeguards, if implemented, would likely improve environmental outcomes. Further, many of the scenarios explored, while being technically feasible, exceeded the level of development that would reasonably occur (see Watson et al., 2024)). These scenarios were included as a stress test of the system and can be useful for benchmarking or contrasting various levels of change. Additional scenarios using mitigation options are further explored and discussed in Section 3.1. 2.2 Ecological modelling and the analysis approach The ecology activity used an asset-based approach to analysis and built upon work presented in Pollino et al. (2018) and Stratford et al. (2024). For the Southern Gulf catchments, 21 ecological assets were selected for analysis. Material in this ecology analysis report should be considered in conjunction with the ecological asset descriptions report (Merrin et al., 2024), which describes the ecology and flow requirements of the assets. The ecological assets spanned freshwater, marine and terrestrial habitats that depend on river flows, and were modelled with regards to changes to surface water (shown in Table 2-2, with individual results and discussion are provided in Section 4). Assets were included if they were distinctive, representative, describable and significant within the catchment. The assets’ flow ecology and locations were described in Merrin et al. (2024) and provides distribution maps. Each asset had different needs from, and linkages to, the flow regime and occurred across different parts of the catchment or the near-shore marine zone. Understanding the flow relationships of assets was important for identifying potential impacts to ecologically important flows. The flow dependencies of assets may consider, for example, life- history needs, habitat suitability, ecosystem functions or behavioural triggers provided by environmental events. The outcome is that assets had different sensitivities to the different manifestations of development. This included whether the changes in flow occurred within the low, medium or high components of the flow regime, while also considering the annual timing of events and the location of the asset in the catchment relative to the change in flow. Together, the suite of assets covered a broad range of flow requirements with different sensitivities to change across the catchment and are indicators of ecology needs or habitat change (such as cease-to- flow). Table 2-2 Ecological assets groups, along with the individual assets, and their associated systems, with the relevant sections of the report For more information on this figure, table or equation please contact CSIRO on enquiries@csiro.au. Domains represent the main patterns of occurrence, and assets may also occur across the other domains. * Bullshark is assessed and contributes to catchment means but it is not discussed as part of Section 4. 2.2.1 Flow dependencies (hydrometrics) modelling The flow dependencies (hydrometrics) assessment provides a consistent, quantitative approach to identify which assets are likely to be impacted by potential changes, based on their flow needs, distribution within the catchment and the type of flow changes resulting from development. For each asset, the modelling calculates an index of flow regime change across different scenarios using asset-specific hydrometrics (Figure 2-2). Merrin et al. (2024) details each asset’s ecology and relationship to flow, including: • habitat dependencies (e.g. floodplain inundation to provide habitat, recharging of groundwater) • life cycle processes (e.g. flow to trigger spawning) • migration and movement pathways (e.g. high flows to enable migration into floodplain wetlands and along the river) • flow to support productivity and food resources (e.g. nutrient plumes into coastal areas). Figure 2-2 Flow dependencies analysis conceptual models for linking flow–ecology relationships for different assets to important parts of the flow regime under hypothetical wet, medium and dry years Biota icons: Integration and Application Network (2023). These flow–ecology relationships were quantified and linked to river hydrology using asset-specific hydrometrics (conceptualised in Figure 2-2 and listed in Appendix C for each asset). Hydrometrics, which are statistical measures of long-term flow regimes (including aspects such as flow magnitude, duration, timing, frequency, and rate of change; see Kennard et al. (2010)), have been broadly used in ecohydrology assessments in national and international contexts for a range of purposes, including water allocation planning, and in ecohydrology research and literature (Leigh and Sheldon, 2008; Marsh et al., 2012; Olden and Poff, 2003). For each asset, a set of hydrometrics that was considered important in supporting its ecology or habitat was selected (see Appendix C for hydrometrics selected for each asset and their definitions). In this analysis, the flow dependencies modelling considered reach and catchment- wide changes in each asset’s important flow dependencies across the subcatchments in which the assets occur, including the near-shore marine zone. Hydrometrics were calculated for each scenario to quantify relative changes in important parts of the flow regime. These changes were expressed as percentile change relative to the distribution of annual values of Scenario AE, calculated over the Assessment period (i.e. 1 September 1890 to 31 August 2022; Figure 2-3). The index of change is calculated as: Percentile change=x − scenario medianscenario median × 100 (1) Where x is the median of metric i, for the hypothetical scenario, and all values are for individual nodes. P646#yIS1 Figure 2-3 Considering scenario change based upon the percentile change from the distribution of each metric under the historical flow at each node See Table 2-3 for the specific rank percentile values for each category. The impact of a hypothetical development on water-dependent ecological assets is inferred and reported here in terms of a habitat-weighted percentile change in asset-specific important flow dependencies. This change is weighted by the habitat value downstream of each node where the asset occurs, and the change in flow dependency is then calculated (Figure 2-4). The weighted values at each node are aggregated to calculate the catchment-wide means of asset flow dependencies (Appendix A). Figure 2-4 Spatial weighting of the metrics for the freshwater-dependent ecological assets in the Southern Gulf catchments Illustration of indicative suitable potential habitat for freshwater-dependent ecological assets along rivers in the Southern Gulf catchments as predicted by species distribution models. High suitability habitat areas are shown in dark blue, while low suitability habitat areas are represented in yellow or light blue. The species distribution models were developed using a combination of Random Forests, Generalised Linear Models (GLMs), and Maxent algorithms (see Merrin et al., 2024). These models were applied to a 2.5 km buffer surrounding the rivers within the Southern Gulf catchments to quantify habitat suitability. The change in the flow dependencies was weighted by habitat suitability for each asset between the river system model nodes of each river reach. P654#yIS1 P657#yIS1 To quantify change, each metric was calculated annually for all water years in the period 1 September 1890 to 31 August 2022 under Scenario AE. This created a distribution for each metric under ‘baseline’ conditions (Figure 2-3). Only metrics that could be calculated on an annual basis were included. Change was calculated as the percentile rank difference for each metric under the scenario of interest relative to the baseline scenario (Scenario AE). This difference provided an index of change, allowing for an understanding of how each metric varied under the scenario compared to Scenario AE, given the historical variability at the site. For each asset, a relative change value was calculated for each selected metrics, and these values were then averaged to obtain a scenario index at each node where the asset was modelled to occur. Conceptually, a scenario index of zero indicates that the mean conditions under the scenario are not different from the mean baseline conditions. A value of 25 indicates that the mean conditions under the scenario are equal to or outside the quartile ranges under Scenario AE (using low flows as an example, the scenario’s mean for the entire period is equivalent to the lowest flow with a 25% annual exceedance probability under Scenario AE). These index values are provided with associated descriptive terms in Table 2-3 (with cut-off values and qualitative descriptors defined by experts) and illustrated as a heat map for reporting (see Figure 2-3 for interpretation of percentile changes). The flow dependencies method enables understanding and quantifying the level of change in each of the important flows metrics for each asset but does not quantify the level of the expected outcomes (e.g. population abundance or condition changes in the asset), the level of sensitivity to the changes, or the level of dependence on flows compared to other environmental drivers such as local rainfall or localised runoff adjacent to the river. Table 2-3 Reporting values for the flow dependencies modelling as rank percentile change of the hydrometrics, considering the change in mean metric value against the distribution observed under Scenario AE PERCENTILE VALUE RATING IMPLICATION >0–2 Negligible The mean for the asset’s metrics under the scenario has negligible change as considered against the modelled historical conditions and lies well within the normal conditions experienced at the model node. The assets’ hydrometrics are within two percentile of the historical Scenario AE mean 2–5 Minor The change is minor with the mean for the asset’s metrics for the scenario between two and five percentile of Scenario AE and the historical distribution of the hydrometrics 5–15 Moderate The change is moderate with the mean for the asset’s metrics under the scenario between five and 15 percentile of Scenario AE and the historical distribution of the hydrometrics 15–30 Major The change is major with the mean for the asset’s metrics for the scenario between 15 and 30 percentile of Scenario AE and the historical distribution of the hydrometrics >30 Extreme The change is extreme, with the mean for the asset’s metrics under the scenario being very different from the modelled historical conditions and metrics occurring well outside typical conditions at the modelled node. The scenario mean is more than 30 percentile from the historical Scenario AE mean One advantage of this method is that multiple attributes of the flow regime (weighted by known importance) are specifically incorporated when considered important for the asset. For example, for an asset that depends on low flows for survival and high flows for breeding and movement, the method would consider both aspects. However, this method does not consider the comparative importance of these aspects nor any correlations between metrics. The method is generalisable across large spatial domains and highly differing flow regimes, and it is robust to different knowledge and data limitations across the broad range of assets. The model does not consider other sources of water, such as rainfall or local discharges of groundwater, where these may be important for supporting ecology. The method provides values of change for each node that can be aggregated (taking the weightings into account) to summarise the mean weighted change occurring across the catchment considering the relative importance of each subcatchment for individual assets. The analysis of flow dependencies does not consider non-linearity, thresholds of change or spatial variability in specific flow requirements (such as the flood magnitudes that inundate floodplains in a specific location) but is generalised across these. The method targets understanding of relative differences between scenarios using Scenario AE as a baseline, rather than absolute values of change. A threshold level of change in the flow regime (generally equivalent to a one-percentile change of the historical distribution for each metric singularly) must be exceeded for at least one of the metrics before the method can detect change. While the method incorporates the range of conditions occurring over the modelled period, it does not explicitly consider event sequencing or predict endpoints such as condition or biomass. To simplify the presentation of the results, the changes in important flow-dependent metrics for each asset were averaged to produce a single (mean) change value for each location where assets were modelled to occur. However, this approach can confound the interpretation of flow dependency changes. To assist interpreting the mean values of asset–flow dependency changes arising from hypothetical development and projected climate change scenarios, these values were compared against an analogue and several benchmarks. The analogue and benchmark values are plotted alongside the hypothetical development and projected climate scenario values to provide context. The Ord River serves as an analogue, offering a comparison to illustrate potential changes in asset–flow dependencies. Changes were calculated by comparing simulated current streamflow levels with pre-European development streamflow in the Ord River near the end-of-system, i.e., below the Ord River Irrigation Area, Lake Kununurra, and Lake Argyle. For comparison, assets in the lower reaches of the Nicholson and Leichhardt catchments are assumed to also occur in the lower Ord River. Three historical low-flow periods are used as benchmarks to assess changes in asset–flow dependencies. These periods represent the lowest 30-year flow (1905–1934), lowest 50-year flow (1900–1949), and lowest 70-year flow (1897–1966) in the historical climate. For example, asset– flow dependencies for the lowest 30-year flow were calculated using Scenario AE between 1905– 1934, relative to Scenario AE. The final comparison is the change in asset–flow dependencies in the Nicholson and Leichhardt catchments since European settlement, calculated by comparing key flow metrics under Scenario AN and Scenario AE. Importantly these comparisons — the Ord River analogue and the historical benchmarks— represent similar flow conditions but are not necessarily equivalent to the outcomes of change if development were to occur. Nonetheless, they do provide some context as to the extent to which asset flow dependencies have changed over long-timer periods in the historical record. The first series of analogues are change in asset–flow dependencies calculated using simulated streamflow of current levels of development relative to streamflow under pre-European levels of development of the Ord River near the end-of-system i.e. below the Ord River Irrigation Area, Lake Kununurra and Lake Argyle. For comparative purposes assets found in the lower reaches of the Nicholson and Leichhardt catchments are assumed to also occur in the lower Ord River. The second series of analogues are the change in asset–flow dependencies calculated over three periods of low-flow conditions relative to the entire baseline. These were the periods with the lowest 30-year flow (1905–1934), lowest 50-year flow (1900–1949) and lowest 70-year flow (1897–1966) across the historical climate. For example, the asset–flow dependencies for the lowest 30-year flow were calculated by using Scenario AE subset between 1905–1934 relative to Scenario AE. The third series of analogues are the change in asset–flow dependencies calculated to occur in the Nicholson and Leichhardt catchments since European settlement (i.e. by comparing key flow dependency metrics under Scenario AN and Scenario AE). 2.2.2 Lateral connectivity modelling Lateral connectivity is the connection of the floodplain to the river channel through inundation associated with a flood event. Lateral connectivity provides an important exchange of nutrients and organic carbon between the floodplain and the river channel, which is important for primary and secondary productivity (Junk et al., 1989; Nielsen et al., 2015). It also allows for the movement of biota and provides habitat for waterbirds, floodplain-dependent fish and other aquatic and riparian species (van Dam et al., 2008a; Ward and Stanford, 1995). The lateral connectivity analysis aimed to understand the potential impacts that each development scenario could have on the connectivity between the river and relevant ecological assets, namely floodplain wetlands, compared to Scenario AE. Directory of Important Wetlands Australia (DIWA) wetlands were used to represent the floodplain wetlands (Figure 2-5). To determine the lateral connectivity of the river to the floodplain, floodplain hydraulics (e.g. depth, velocity) and inundation dynamics were modelled using MIKE 21 Flow Model FM (see companion technical report on flood modelling, Karim et al. 2024). The model domain has an area of 17,130 km2 and includes the floodplains of the Nicholson, Gregory and Leichhardt rivers (Figure 2-5). The model was run with a 5 m resolution digital elevation model across approximately 43.7% of the floodplain along the Nicholson, Gregory, Albert and Leichhardt rivers, with 30 m resolution data used for the remaining modelling domain. Two individual flood events were modelled. A 2016 flood event was modelled representing a 33.3% annual exceedance probability (AEP) event (i.e. a flood event that occurs, on average, 1 in 3 years), and a 2023 flood event representing a 2.6% AEP event (i.e. a flood event that occurs, on average, 1 in 38 years) (Karim et al., 2024). Each flood event was simulated for 30 days to ensure both the rising and falling limbs were included in the outputs. Figure 2-5 Locations of the floodplain wetlands used in the lateral connectivity analysis The hydrodynamic model domain is described in Karim et al. (2024). The numbers refer to the wetlands that were used in the analysis (see Section 4.4.1). Datasets: Department of Agriculture, Water, and the Environment (2021a), Geoscience Australia (2017). P702#yIS1 The hydrodynamic scenarios modelled are detailed in Karim et al. (2024) and are specific to the lateral connectivity section. They include development scenarios (dam scenarios (B-D) and water harvesting scenarios (B-W)), future climate scenarios (CEdry and CEwet) and combinations of future climate and development scenarios (Ddry-D and Ddry-W). For the dam scenarios (B-D), the Gregory River and Gunpowder Creek dams were modelled, as well as an additional dam on the Nicholson River (see Karim et al. (2024)). At the start of each flood event, each dam was set to 50% capacity. For the water harvesting scenarios (B-W), extraction occurred at five nodes with an extraction target of 150 GL per year, an assumed pump start threshold of 600 ML/day, and a pumping rate of 20 days to reach the extraction target (Table 2-4) (see Karim et al. (2024)). The results for the lateral connectivity analysis are shown in Section 4.4.1. Table 2-4 Water resource development and climate scenarios explored in this ecology analysis Karim et al. (2024) describes the hydrodynamic modelling and additional scenario details. SCENARIO DESCRIPTION AE Historical climate and current development scenario (surface water, groundwater and economic development were assumed as of 1 July 2023). B-D Historical climate and hypothetical future development – instream dams. Dams were located on the Gregory River, Gunpowder Creek and Nicholson River. At the start of each flood event, each dam was set to 50% capacity. B-W Historical climate and hypothetical future development – water harvest extraction. Five water extraction nodes were used in the model, with an extraction target of 150 GL per year, an assumed a pump start threshold of 600 ML/day, and a pumping rate of 20 days to reach the extraction target. CEdry Future climate and current level of development – 10th percentile exceedance changes in rainfall. CEwet Future climate and current level of development – 90th percentile exceedance changes in rainfall. Ddry-D Future climate and hypothetical future development – The climate scenario used is CEdry with the hypothetical future development the same as B-D. Ddry-W Future climate and hypothetical future development – The climate scenario used is CEdry with the hypothetical future development the same as B-W. 3 Catchment results and implications This section provides an overview of how changes in flow regimes resulting from water resource development could affect environmental assets of the Southern Gulf catchments and the near- shore marine zone. Hypothetical flow scenarios, including water harvesting and instream dams are used to represent different potential pathways of development (see Section 2.1.1 for terminology and Section 2.1.2 for scenario definitions). Changes in flow regimes can have impacts across a broad range of flow dependent ecological assets which can extend considerable distances downstream from the source of change and onto floodplains. The flow requirements (such as the magnitude, timing, duration and frequency of both low and high flows) of different species and habitats vary. Flow dependencies modelling considers the location of 21 ecological assets across 79 nodes in the Southern Gulf catchments, including the end-of-system node for near-shore marine assets (Figure 2-1 and Appendix A). The scenarios explore how different changes in flow associated with the type, location and management (including mitigation strategies) of water resource development could affect ecological assets relative to Scenario AE. 3.1 Water resource development scenario result overviews This section provides a high-level overview of the scenarios showing aggregated results (mean of assets) and discusses specific differences in the spatial pattern and magnitude of change driven by the scenarios and given the range of outcomes across the modelled environmental assets with a focus on understanding broad changes in asset flow dependencies with different development pathways and mitigation measures. Outcomes for specific assets vary depending upon flow- requirements and flow-ecology and are discussed with implications and interpretation of results for individual assets in Section 4. The values associated with the catchment means include, but do not show, the range in outcomes across assets, where change in important flows for individual assets or at specific locations can be considerably higher or lower than the mean. 3.1.1 Scenario trends and summaries for the catchment Hypothetical dams and water harvesting resulted in different changes in flows, affecting outcomes for ecology by different magnitudes of change in flow dependencies across different parts of the Southern Gulf catchments, and in different ways (Figure 3-1 and Figure 3-2, and see Section 3.1.2 for changes in flow dependencies for water harvesting and Section 3.1.3 for dams). The level of impact from water resource developments depended on the type, the extraction volume and the mitigation measures implemented. For example, the water harvesting scenario with the highest catchment mean change in assets flow dependencies was Scenario B-WT150P200R30E0 (Figure 3-2). In this scenario, mud crabs, mullet, threadfin and prawn species were assessed at the end-of-system node, showing moderate changes in their mean important flow dependencies averaged across their respective nodes (see respective asset sections for more details). This is likely due to the low pump-start threshold in this scenario, combined with the assets’ sensitivity to low flow regimes (when water levels are low). The largest site-specific changes in asset flow dependencies under water harvesting, ranged from major to extreme, impacting colonial and semi-colonial wading waterbirds, inchannel waterholes and surface-water- dependent vegetation (see sections 4.2.1, 4.4.2 and 4.4.6 and see Table 2-2 for groupings). While water harvesting can lead to greater changes in flow dependencies in some locations, it also provides more opportunities for mitigating these impacts through adjustments to thresholds, pump rates, and extraction target volumes. Large instream dams had a greater mean impact on surface-flow-dependent ecology averaged across the Southern Gulf catchments than water harvesting, but the specific location of the dam also influenced the magnitude of these impacts. Scenario B-DGR, a hypothetical dam on the perennial Gregory River (Figure 2-1), resulted in a minor catchment-wide change (4.2; Figure 3-2), yet caused major impacts on downstream flows that are critical to several species and groups, including mullet, mud crabs, and prawn species. In contrast, Scenario B-DGPC, a hypothetical dam on Gunpowder Creek, a major tributary of the Leichhardt River (Figure 2-1), showed a negligible mean impact across the catchment (0.9; Figure 3-2) and had negligible to minor impacts on important flows for most assets. However, local effects (i.e. nodes) were often more pronounced, sometimes reaching extreme levels. Scenario B-DGR resulted in a higher impact than an equivalent water harvest scenario with similar levels of water extraction (Scenario B-WT150P200R30E0), which had a negligible change (1.5). Under Scenario B-DGR, 15% of the nodes were rated as moderate or higher change in flow dependencies across all the assets, compared to 13% of the nodes under Scenario B-WT150P200R30E0 and 6% of the nodes in Scenario B-DGPC (Figure 3-3). The percentage of nodes with major change or greater was 11% under Scenario B-DGR compared with 2% under Scenario B- WT150P200R30E0 and only 1% of the nodes in Scenario B-DGPC (Figure 3-3). For scenarios with dams, the largest site-based changes in assets flow dependencies were often directly downstream of hypothetical dams with up to extreme change in important flow dependencies for assets including inchannel waterholes, cryptic wading waterbirds, sawfishes and grunters below the Gregory River dam. For assets below the hypothetical Gunpowder Creek dam, flow dependency changes at downstream sites were lower, but still extreme for colonial and semi- colonial wading waterbirds and surface-water-dependent vegetation (see sections 4.2.1 and 4.4.6). For freshwater species that are found across large parts of the Assessment area, catchment mean change in flow dependencies are often reduced in association with increased distribution across unaffected river reaches. Under the modelled drying climate scenario (Scenario CEdry), the change in asset flow dependencies was moderate for the mean of all assets (Figure 3-1e). Unlike the hypothetical dam or water harvest scenarios, Scenario CEdry affected the flow at all nodes in the Assessment region. The mean change in flow dependencies was higher than in any of the instream dam scenarios (i.e. B-DGR, B-DGPC, or B-D2) or B-WT150P200R30E0. For example, 55% of the nodes moderate or higher changes, while 14% showed major changes and 0.5% of the nodes showed extreme changes in flow dependencies across all assets (Figure 3-3). The combined cumulative effects of water resource development and the dry future climate scenario (D-WT300P600R30E0) led to the greatest catchment-level changes with moderate change to asset flow dependencies (Figure 3-2). Figure 3-1 Spatial heatmap of change in asset flow dependencies across the Southern Gulf catchments considering change across all assets in the locations which each asset is assessed Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the mean level of flow change of all assets’ important metrics weighted by the habitat value of each reach for each asset. P744#yIS1 Figure 3-2 Mean change to important flow dependencies for assets across scenarios and nodes Horizontal grey bars and number correspond to the mean change across all model node locations. Colour shading indicates the mean level of flow change of all assets’ important metrics weighted by the habitat value of each reach for each asset. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios (see Table 2-1) are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50- year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Leichhardt catchment that has already occurred since European settlement. P747#yIS1 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% BDGCBDGRBWT150P200R30E0CEDryNegligible Minor Moderate Major Extreme Figure3-3Percentage of nodes with different changes in important asset flow dependencies aggregated for allassets See Table2-1for interpretation of scenarios. 3.1.2Water harvesting For water harvesting scenarios,several measurescan helpmitigate theimpacts of flow-relatedchangesfromwaterextraction.Theseincludelimiting system targets to reduce extraction acrossthe catchment,implementinga pump-start threshold to restrict pumpingduring low river flows, settinganannual diversion commencement flow requirementto allow a volume of water to passthrough the system before pumpingeach water year, and limiting thepump rateforwaterextractionfrom the river (see Section2.1.1andGibbs et al. (2024b)for more details).These measures improve environmental outcomes compared to scenarioswithoutthem. Reducing systemtargets decreases the changes in flows across asset groups, while largerextractionvolumes lead to moderate increases in flow dependencies across the catchment’s ecological assets(Figure3-4andFigure3-5). Asset groups like turtles, prawns and other speciesandthe marineassets,experience greater changes at higher system targets of 400to 500 GL/year(Figure3-4) (see Appendix Bfor details on individual assets).Providing minimum flow thresholdsor annual diversion commencementrequirements canhelp mitigate these changes.For example, an annual diversion commencementflow requirement of 250 GL improves ecological flows acrossasset groups,with smaller requirementsproportionally reducing flow changes(Figure3-4andFigure3-5).The largest benefitsfor smaller irrigationtargets are often seen with an initial 100 GLrequirement, asthis delaysthestart of pumping, retaining earlywet-season flows and shorteningthe waterharvest period (Gibbs et al., 2024b). While improvements are likely to occur inconjunction with providing either minimum flow thresholds oranannual diversioncommencement flow requirement, greater extractionequates to a greater level offlow change inimportant ecologicalflow metrics. Chapter3 Catchment results and implications|25 Figure 3-4 Mean change associated with each asset’s important metrics across water harvesting increments of system target and pump-start threshold with no annual diversion commencement flow requirement and pump rate of 30 days Colour intensity represents the mean level of change in important flow dependencies with the scenario given the habitat importance of each node for each asset. P756#yIS1 Figure 3-5 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and pump-start threshold for an annual diversion commencement flow requirement of 250 GL and pump rate of 30 days Colour intensity represents the mean level of change in important flow dependencies with the scenario given the habitat importance of each node for each asset. P759#yIS1 Providing minimum flow pump-start thresholds reduced the level of change in important flow dependencies across increasing threshold levels (Figure 3-4). Increasing the pump-start threshold from 200 to 1000 ML/day significantly reduces changes in flow dependencies across several asset groups, particularly for fish, sharks, rays and freshwater-dependent habitats (Figure 3-4 and Appendix B). Compared to lower thresholds of 200 or 400 ML/day, the improvements are particularly notable beyond 600 ML/day, where the impacts on freshwater-dependent habitats and marine environments are reduced (Figure 3-4 and Appendix B). Adjusting the pump-start threshold can also mitigate the effects of higher extraction volumes. For example, the change in flow dependency under the B-WT150P200E0 scenario was almost the same as B-WT300P600E0 (Figure 3-2), highlighting that doubling the extraction volume has a similar impact on important flow dependencies, provided the threshold increases from 200 to 600 ML/day. Higher pump-start thresholds appear most effective when system targets are limited to about 200 GL/year and when annual diversion commencement requirements are low or absent, as substantial flows may have already passed through the system before the pump threshold is activated (Figure 3-5 and Appendix B). This indicates that optimising the pump-start threshold is crucial for reducing the strain on ecosystems, particularly during periods of low flow. Annual diversion commencement flow requirements provide for a specified volume of water to pass through the last node in the river system model before pumping for water harvesting can commence. The outcome associated with providing annual diversion commencement flow requirements occurs by delaying the start of pumping to later in the wet season, thus retaining initial wet-season flows while also reducing the period of time available for water harvest (Gibbs et al., 2024b). In this analysis, different annual diversion commencement flow requirement volumes (ranging from 0 to 800 GL) were modelled. Providing an annual diversion commencement flow requirement of 500 GL improved ecological flows broadly across asset groups in comparison to having no annual diversion commencement flow requirement, considering asset means across all their assessment nodes (e.g. compare Figure 3-5 with 250 GL annual diversion commencement flow and Figure 3-4 with no such flow). Smaller annual diversion commencement flow requirement volumes were found to proportionally reduce change in asset flow dependencies and larger volumes of annual diversion commencement flow requirements provided additional benefit. However, for smaller irrigation targets, the largest gain was often achieved with the initial 100 GL requirement (Figure 3-6). Varying levels of end-of-system (EOS) flow requirements also affect different ecological assets in relation to system targets. For fish, sharks and rays, increasing the EOS requirement from zero to 250 GL/day results in minimal changes in flow dependencies, particularly at lower system targets (~100 GL/year). However, other groups, such as turtles, prawns and other species, experience significant impacts at lower EOS requirements (below 50 GL/day). As the EOS requirement increases, these impacts are mitigated, especially at higher system targets. Flow-dependent habitats and marine environments are also sensitive to lower EOS requirements, with notable reductions in ecological change when the EOS requirement reaches 150 to 250 GL/day. Overall, impacts are more evenly distributed across EOS requirements with the least change occurring when EOS requirements are higher and system targets are kept below 200 GL/year. This suggests that increasing EOS flow requirements provides substantial ecological benefits, particularly for groups sensitive to flow changes, such as turtles, prawns and flow-dependent habitats (Figure 3-6). Figure 3-6 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and end-of-system (EOS) requirement with a pump threshold of 200 ML/day and a pump rate of 30 days Colour intensity represents the mean level of change occurring in the asset’s important flow metrics with the scenarios given the habitat importance of each node for each asset. In this figure, EOS refers to the annual diversion commencement flow. P767#yIS1 Setting pump capacity limits on the rate that water can be extracted showed that changes in important asset flow dependencies under water harvest are reduced when pump rates are lower (Figure 3-7). As water can only be extracted when river flow exceeds the minimum pump start threshold, this limits the volume of water that can be extracted during a wet season and reduces the impact at the commencement of pumping (i.e. on any day the extraction volume is limited, but pumping may extend to later in the season). Additionally, at larger extraction target volumes, limiting the pump capacity often resulted in an increase in the number of years that the extraction target volume was not achieved (Gibbs et al., 2024b), which could reduce the extent of change for ecology. Figure 3-7 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and pump rate with a pump threshold of 200ML/day and a pump rate of 30 days and no annual diversion commencement flow requirement Colour intensity represents the mean level of change occurring in the asset’s important flow dependencies with the scenarios given the habitat importance of each node for each asset. P771#yIS1 3.1.3 Instream dams with and without transparent flows Two hypothetical locations for instream dams (Gregory River and Gunpowder Creek) were selected for modelling and analysis (Yang et al., 2024) and simulated following the hydrology modelling approach outlined in Gibbs et al. (2024b). Their locations are shown in Figure 2-1. The objective of this analysis is to test the effect of different dam locations and configurations on changes to streamflow to understand the effect on downstream flow ecology. Dams are modelled individually (i.e. scenarios B-DGR and B-DGPC) as well as both together (Scenario B-D2) to better understand cumulative impacts and to have variants with and without the mitigation measure of providing transparent flows (see Section 2.1.1 for definitions). Instream dams create a range of impacts on streamflow associated with the capture and extraction of water, affecting the timing and magnitude of downstream flows. The changes in downstream flow associated with instream dams are explored here across broad asset groups, and results are shown as the mean of asset values. Impacts associated with loss of connectivity due to the dam wall and loss of habitat associated with the dam inundation extent are discussed in Yang et al. (2024). The dam scenarios and the resulting ecology flow dependencies are discussed in more detail for each asset in Section 4. Impacts on flow directly downstream of modelled dams can often be high and may cause extreme changes in ecological flow dependencies. Reaches of river further downstream have contributions of water from unimpacted tributaries, and for the marine region, flows from other catchments that help support ecological outcomes of flow regimes. Dams further up the catchment may, however, affect a larger proportion of streams and river reaches in terms of flow regime change, but they may have lower impacts in terms of connectivity. The Southern Gulf catchments, in particular, are complex and braided, and they have many tributaries that may be unimpacted within the freshwater sections of the catchment. Effects on important flows are not equivalent across assets, and large local impacts may lead to changes in ecology across other parts of the catchment due to the connected nature of ecological systems. The cumulative change in flow dependencies associated with two hypothetical dams (Scenario B-D2) are greater than those of individual dams (Table 3-1). However, the largest contribution of change to Scenario B-D2 originates from Scenario B-DGR. Measures to mitigate the flow-related impacts of large instream dams, such as transparent flows (inflows let to pass the dam wall for environmental purposes; see Section 2.1.1 and Table 2-1), resulted in lower change to ecological flow dependencies broadly across all assets compared to scenarios without these measures (Table 3-1). Particularly strong benefits were shown as reduced change to important flow dependencies resulting from transparent flows for members of the fish and waterbird groups (see Table 2-2 for groupings). Instream dams capture inflows and change downstream flow regimes. Transparent flows are a type of environmental flow provided as releases from dams that mimic or maintain natural flows. They can be successful in replicating smaller to moderate flood events during periods when natural runoff is entering the dam impoundment. Staged offtakes to maintain natural water temperatures are required. However, providing transparent flows from a dam it lowers the volume of water in storage and thereby increases the capture of early flood events in the following year. This might also result in lowering flood peaks, resulting in smaller inundation events during periods of floods affecting assets such as surface-water-dependent vegetation (see Section 4.4.6). Modelling transparent flows uses inflow thresholds on dams andwas designed primarilyto preserve lower flowsduringperiodsofnaturalinflow. Inflow thresholds used in thetransparentflows analysiswere similar to the commence-to- pump thresholds used in water harvest scenarios, facilitating comparison. Transparent flowsareprovidedacross bothindividual damsandin Scenario B-D2(Gibbs et al.,2024b). Table3-1Scenarios of different hypothetical instream dam locationsshowingmean changes of ecologyflows forassetsgroups, assessedat theirrespective catchment nodes Higher values represent greater change in flows important to the assets of each group. Values are asset means acrosstheir respective catchment assessment nodes (see Appendix A). Some assets are considered in multiple groups,forwhichthe mean across the nodes is used. Asset means include values from all nodes thatthe asset is assessed in, including in reaches that may not be affected by flow regime change. SCENARIODESCRIPTIONALL ASSETMEANFISHWATERBIRDSOTHERSPECIESHABITATSFRESHWATERASSETSMARINEASSETS B-DGPCGunpowder Creek0.8 0.4 0.8 0.1 0.5 0.8 0.2 dam B-DGPCTGunpowder Creek0.7 0.3 0.7 0.1 0.5 0.7 0.2 dam with transparent flows B-DGRGregory River dam4.1 8.9 4.0 18.2 6.4 4.4 12.7 B-DGRTGregory River dam1.5 1.8 1.7 3.8 2.8 1.7 3 with transparent flows B-D2Both B-DGPCand B-4.5 9.1 4.4 18.3 6.7 4.7 12.8 DGR B-D2TBoth B-DGPCand B-2.7 5.5 2.7 12.7 4.1 2.9 8.4 DGRwith transparent flows Chapter3 Catchment results and implications|33 4 Asset assessments This section provides an overview and discussion of the modelling results for the prioritised ecological assets across a subset of scenarios. Asset outcomes consider their water needs, distribution within the catchment and the range of flow conditions occurring under each of the scenarios using a range of different methods and provide a discussion on the ecological context of the change in flows for the asset. The scenarios used in the asset results are selected to reflect different hypothetical pathways of development. Many of the scenarios have no environmental flow provisions and can be viewed as providing a pessimistic estimation of impacts on ecological flow dependencies to highlight the potential stress points associated with the development option and possible outcomes if there is no mitigation or no regulatory compliance (see Section 2.1 for a description of the scenarios). Section 3.1 provides an overview of the influence of providing mitigation strategies in association with the water resource development scenarios. 4.1 Fish, sharks and rays The fish, sharks and rays group comprises six ecological assets including barramundi, catfish, grunters, mullet, sawfishes and threadfin. The members of this group are obligatory aquatic species that can inhabit freshwater, marine waters or a combination of both. Members of this group can have flow associations to support function and important life-history phases. Some members of this group, including barramundi, require movement between freshwater and marine habitats to support life cycle processes, as well as connectivity between the river and floodplain habitats. Some members, such as grunter species, require specific flow and habitat conditions such as riffle habitat to support different life stages. Refuge habitats during the dry season can be important for some species within this group. 4.1.1 Barramundi Barramundi are large opportunistic-predatory fish that inhabit riverine, estuarine and marine waters in northern Australia, including those in the Southern Gulf catchments. Adults mate and spawn in the lower estuary and coastal habitats near river mouths during the late dry season and early wet season. Small juveniles migrate upstream from the estuary to freshwater habitats where they grow and mature before emigrating downstream as adults to estuarine habitats where they reside and reproduce (Roberts et al., 2019). In the Southern Gulf catchments, barramundi occupy relatively pristine habitats in both freshwater and estuarine reaches, as well as coastal marine waters. Their life history renders them critically dependent on river flows (Tanimoto et al., 2012) as new recruits move into supra-littoral estuarine and coastal salt flat habitats, and juveniles occupy freshwater riverine reaches and wetland habitats (Crook et al., 2016; Russell and Garrett, 1985; Russell and Garrett, 1983). Barramundi are sensitive to changes in flow regime. Critical requirements affecting growth and survival include riverine–wetland connectivity, riverine–estuarine connectivity, passage to spawning habitat and volume of flood flows (Crook et al., 2016; Roberts et al., 2019). In years of natural low flows, or flows reduced by anthropogenic activity, the range of facultative habitat and ecosystem processes available to barramundi is reduced, reducing growth and survival (Leahy and Robins, 2021; Robins et al., 2006; Robins et al., 2005). Barramundi is an ecologically important fish species capable of modifying the estuarine and riverine fish and crustacean communities throughout Australia’s wet-dry tropics (Blaber et al., 1989; Brewer et al., 1995; Milton et al., 2005). It is targeted by commercial, recreational and Indigenous fisheries. Barramundi is an important species for Indigenous Peoples in northern Australia, both culturally (Finn and Jackson, 2011; Jackson et al., 2011) and as a food source (Naughton et al., 1986). The analysis considers change in flow regime and related habitat changes but does not consider the addition or loss of potential habitat associated with the creation of a dam impoundment or instream structures (see also Yang et al. (2024) for dam impoundments). Asset flow dependencies analysis Barramundi were assessed across a total of 3948 km of river reaches in the Southern Gulf catchments and in the marine region with contributing flows from 63 model nodes (see Appendix A). Some of the key river reaches for barramundi within the Assessment catchments were modelled downstream of nodes 9129042 and 9121161 and at the combined end-of-system node 9100000. Locations were selected for modelling barramundi based upon species distribution models (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for barramundi. For the mean weighted change in important flow metrics across all 63 barramundi analysis nodes, the hypothetical dam scenarios ranged from negligible (0.6) to minor (3.4) for change in flow under scenarios B-DGPCT and B-D2, respectively. In contrast, water harvesting scenarios showed negligible variations, with values ranging from 0.5 under Scenario B-WT50P600R30E250 to 1.2 under Scenario B-WT150P200R30E0. Scenario CEdry resulted in a moderate change (5.3) in important flow metrics for barramundi. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for barramundi (Figure 4-1). Under scenario D2, the largest contributing change in important flow dependencies was for the metric low flood pulse count (<90th percentile) at node 9121015. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean November discharge at node 9121015. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for barramundi. Figure 4-1 Spatial heatmap of habitat-weighted changes in flow for barramundi, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for barramundi weighted by the habitat value of each reach. P864#yIS1 Water harvesting and changes in important flows for barramundi The hypothetical water harvesting scenarios resulted in a negligible mean change across barramundi assessment nodes, ranging from 0.5 to 1.2 under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Under the water harvest scenario that resulted in the largest catchment mean change (Scenario B-WT150P200R30E0), the single node with the highest change was 9121015 with moderate change (11.6) in important flow metrics. However, flow change across the catchment was generally much less than this. The change in important flows for barramundi under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-2). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the average weighted change in flows across the catchment was negligible (0.5). It increased to 0.9 (negligible) when the extraction target volume was increased to 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the negligible change across the assessment nodes from 1.2 to 0.7 (Figure 4-2). Measures to protect important parts of the flow regime can support catchment ecology, for example, other modelling studies show that reducing the extraction target volume of water in any water year results in better outcomes for the barramundi population (Plagányi et al., 2024). In addition, model results from other studies indicate that increasing the pump-start threshold protects the low flows that are important for barramundi ecology (Plagányi et al., 2024). Figure 4-2 Habitat-weighted change in barramundi flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for barramundi. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P869#yIS1 Dams and changes in important flows for barramundi Under the hypothetical dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.6) in important flow metrics when averaged across the 63 barramundi assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important river flows for barramundi was similarly negligible (0.6). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a minor (3.2) weighted change in important flows averaged across the assessment nodes. The impact on barramundi was reduced to negligible (1.2) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a minor (3.4) mean change occurred across the catchment without transparent flows. The impact of flow reduction was reduced to minor (2.6) with provision of transparent flows under Scenario B-D2T. Scenario B-D2 (with two dams) resulted in marginally larger mean flow change across the catchment than either of the single-dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing transparent flows to support environmental outcomes for barramundi (Figure 4-2). During their freshwater juvenile and young-adult life phases, barramundi populations depend on habitat connectivity being maintained throughout the catchment, in both upstream riverine and palustrine monsoon-season habitat. The physical barriers of instream dam infrastructure, and reduced overbank flows due to impounded floodwaters, limit both habitat extent and habitat connectivity. Under dam scenarios, habitat-weighted changes in important flows for barramundi were greatest at node 9121015 (Figure 4-2) with a change in important flows at this single node recorded as extreme (37.5). Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR resulted in moderate (10.2), and major (27.2) percentile change in important flows, respectively. These changes were reduced to moderate (8.4 and 8.2, respectively) when modelled with transparent flows (scenarios B-DGPCT and B-DGRT, respectively). This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing transparent flows to support low-level flows and first-of-the-season flows that provide ecosystem service outcomes. Climate change and water resource development for important flows for barramundi Scenario CEdry resulted in a moderate change (5.3) in important flow metrics for barramundi considering the mean across the 63 barramundi assessment nodes (Figure 4-2). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.6) and B-WT150P600R30E0 (negligible; 0.7). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate change (both 5.8) when weighted across all barramundi assessment nodes. This shows that the combined changes of scenarios D-DGPC or D-WT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT150P200R30E0 alone. Barramundi populations depend on habitat connectivity being maintained throughout the catchment. Physical barriers of instream infrastructure (e.g. under scenarios B-DGPC or B-DGR) would limit access to some riverine habitats (see Yang et al. (2024)). Access to upstream habitats and estuarine supra-littoral habitats would be reduced if water harvesting or dam scenarios reduced the inundation level, frequency and duration of overbank flows. High river flows expand the extent of wetland and estuarine-margin habitats, increase connectivity, deliver nutrients from terrestrial landscapes, create hot spots of high primary productivity and food webs, increase prey productivity and availability, and increase migration within the river catchment (Burford et al., 2016; Burford and Faggotter, 2021; Leahy and Robins, 2021; Ndehedehe et al., 2020a; Ndehedehe et al., 2021). Reduced flow levels under a future drier climate would reduce wetland habitat connectivity and productivity. The difference in flow effects of single dams (negligible) or two dams (minor) are expected as the single-dam scenarios reduce flow by a relatively minimal amount and do not affect most subcatchments. In both cases, the construction of dam infrastructure will reduce barramundi habitat by reducing both catchment connectivity and flows (see Yang et al. (2024) for changes associated with instream structures). However, many subcatchments would not be affected. Water extraction of between 50 and 300 GL (i.e. scenarios B-WT50P600R30E250 to B-WT300P600R30E0) also has a negligible impact on barramundi due to minimal flow reduction, including both wet-season high-level flows and low-level flows during September to March prior to the wet season. Barramundi growth and year-class strength are enhanced by large wet-season flows during the wet-season months of January to March (Crook et al., 2022; Leahy and Robins, 2021). Larger flows both preceding and following the wet-season peak flows also enhance barramundi growth and recruitment. Previous studies have shown that reduced high flows lowers growth rates of barramundi: a model of flow–growth estimates a 12% reduction in barramundi growth under an 18% reduction in natural flow regime (Leahy and Robins, 2021). Recent research on monsoon- driven habitat used by barramundi has shown that, during drier years with lower river flows, a large proportion of the juvenile barramundi immigrate upstream from estuarine spawning habitat to freshwater habitats, probably seeking out riverine and palustrine productive hot spots (Ndehedehe et al., 2020b; Roberts et al., 2023). Hence, maintaining low-level flows would be critical. The moderate change to important flows and reduced flow levels, shortened duration of the peak flows, and disruptions to natural seasonal flow patterns caused by water harvesting are likely to decrease barramundi populations within catchments subject to water resource development. Results from modelling in other Australian tropical catchments at similar latitudes, show impacts on barramundi populations from modifying the level and seasonality of flows due to the construction of dams or water extraction to support irrigation (Plagányi et al., 2024). 4.1.2 Catfish Catfish are a diverse group of fish that inhabit both inland and coastal waters globally. In northern Australia, some catfish species are freshwater, some are marine and some move between the river and the estuary (Pusey et al., 2020). Catfish in the Southern Gulf catchments belong to two families: Ariidae (seven species, including marine and freshwater) and Plotosidae (four species, mainly freshwater in the Southern Gulf catchments). The larger-bodied ariid catfish like Neoarius graeffei (fork-tailed catfish), N. midgleyi and Sciades paucus, are mainly found in the main stems of the Southern Gulf catchment. The usually smaller-bodied Neosilurus species (in the Plotosidae) are mainly found in smaller tributaries. While not as important as barramundi or sooty grunter (Leiopotherapon unicolor), the fork-tailed catfish has considerable importance as a subsistence fish for Indigenous communities (Finn and Jackson, 2011; Jackson et al., 2011). The key threats to the two most common Neosilurus species are associated with potential instream barriers causing changes in downstream flow and loss of connectivity. Plotosidae need high flows to trigger spawning migration, and they require a barrier-free passage to spawning grounds in the headwater streams (see Yang et al. (2024) for changes associated with instream structures). Asset flow dependencies analysis Catfish were assessed across a total of 3948 km of river reaches in the Southern Gulf catchments with contributing flows from 62 model nodes (see Appendix A). Some of the key river reaches for catfish within the Assessment catchments were modelled downstream of nodes 9121010, 9121111 and 9130030. Locations were selected for modelling catfish based upon species distribution models of the fork-tailed catfish (Neoarius graeffei) (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for catfish. When considering the mean weighted change in important flow metrics across all 62 catfish analysis nodes, the hypothetical dam scenarios ranged from negligible (0.5) to moderate (6.1) for change in flow under scenarios B-DGPCT and B-D2, respectively. In contrast, water harvesting scenarios ranged from negligible (0.5) to minor (2.1) under scenarios B-WT50P600R30E150 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (5.3) in important flow metrics for catfish. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for catfish (Figure 4-3). Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 30-day means of daily discharge at node 9121010. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric Annual minima of 90-day means of daily discharge at node 9121010. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow- ecology relationships for catfish. Figure 4-3 Spatial heatmap of habitat-weighted changes in flow for catfish, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for catfish weighted by the habitat value of each reach. P887#yIS1 Water harvesting and changes in important flows for catfish The hypothetical water harvesting scenarios resulted in a mean change across catfish assessment nodes from negligible (0.5) to minor (2.1) under scenarios B-WT50P600R30E150 and B-WT150P200R30E0, respectively. Under the water harvest scenario which resulted in the largest catchment mean change (Scenario B-WT150P200R30E0), the single node with the highest change was 9121010 with major change (25.2) in important flow metrics (Figure 4-4). However, flow change across the catchment was generally much less than this. The change in important flows for catfish under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-4). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the mean weighted change in flows across the catchment was negligible (0.5). It increased to 0.6 (negligible) when the extraction target volume was increased to 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes from minor (2.1) to negligible (0.5) (Figure 4-4). Implementing measures to protect key aspects of the flow regime can significantly support ecological health. Reducing the extraction target volume and raising the pump-start threshold helps preserve low flows critical to catfish ecology. However, dam infrastructure, water extraction and river regulation can disrupt seasonal flow patterns, leading to longer cease-to-flow periods and reduced overbank flows. These flow modifications pose a major threat to catfish by limiting access to riverine habitats and decreasing the frequency of floodplain connections that are essential for juvenile recruitment (Allen, 1982; Bishop et al., 1990). Figure 4-4 Habitat-weighted change in catfish flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for catfish. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P892#yIS1 Dams and changes in important flows for catfish Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.7) in important flow metrics when averaged across the 62 catfish assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for catfish was reduced to negligible (0.5). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a moderate (5.9) weighted change in important flows averaged across the assessment nodes. This was reduced to negligible (0.8) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a moderate (6.1) mean change occurred across the catchment without transparent flows. This was reduced to negligible (1.7) with provision of transparent flows (Scenario B-D2T). Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either single-dam scenario. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for catfish. Under dam scenarios, habitat-weighted changes in important flows for catfish were greatest at node 9121010 (Figure 4-4) with a change in important flows at this single node recorded as extreme (75.4). Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR resulted in minor (4.6) and extreme (52.5) percentile change in important flows, respectively. However, when transparent flows were modelled (scenarios B-DGPCT and B-DGRT), these changes were reduced to negligible (0.8) and minor (3.6), respectively, at these same nodes. This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes. Additionally, instream infrastructure that blocks upstream movement and captures high-flow events disrupts spawning migrations, posing additional risks to catfish (see Yang et al. (2024) for details on these impacts). Climate change and water resource development for important flows for catfish Scenario CEdry resulted in a moderate change (5.3) in important flow metrics for catfish considering the mean across the 62 catfish assessment nodes (Figure 4-4). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.7) and B-WT150P600R30E0 (negligible; 0.5). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate change (5.9 and 5.6, respectively) when weighted across all catfish assessment nodes. This shows that the combined changes of scenarios D-DGPC or D-WT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160t200r30 alone. Four of the identified key threats for catfish can be found across the catchment. Flow modification can result from water harvesting, dam infrastructure and river regulation, and there is the added threat of climate change. All catfish species depend on connections to the floodplain, often for the purpose of juvenile recruitment. River regulation and water extraction can reduce overbank flows, leading to a decrease in connection frequency and therefore a loss in recruitment opportunities (Allen, 1982; Bishop et al., 1990). Some Plotosidae species prefer flowing water in the main channel. The construction of instream infrastructure that inhibits upstream movement and captures high-flow events removes the pathways and stimulus for spawning migrations, providing additional risks to catfish (see Yang et al. (2024) for changes associated with instream structures). In addition, seasonal flow patterns are affected by dam infrastructure or extraction, both of which may increase cease-to-flow periods and thereby limit access to riverine habitats (Allen, 1982; Bishop et al., 1990). The combination of impacts on fish movement and the loss of spawning migration triggers from reduced flow, is highly likely to affect population sizes of Plotosidae, especially Neosilurus ater (Pusey et al., 2004). Thermal impacts on catfish habitat also may affect upstream populations. Despite limited data on tropical catfish, Pusey et al. (2004) hypothesises that, in upland areas, winter thermal tolerances of Neoarius graeffei are close to their thermal limit. Cold-water releases from bottom water in a stratified dam may breach temperature tolerances of tropical catfish and cause mortality. 4.1.3 Grunters Grunters include a total of 37 species from 11 genera, of which the most species-rich genera are Hephaestus, Scortum, Syncomistes and Terapon. Grunters inhabit riverine, estuarine and marine waters in northern Australia. The sooty grunter (Leiopotherapon unicolor) is an important recreational species for which environmental flow is managed to maintain suitable habitat conditions in some modified river systems (Chan et al., 2012). Grunters are also important species for Indigenous Peoples in northern Australia, both culturally (Finn and Jackson, 2011; Jackson et al., 2011) and as a food source (Naughton et al., 1986). In the Southern Gulf catchments, grunters are fairly ubiquitous with headwaters being spawning and nursery grounds as well as being habitat for adults of the smaller species (e.g. spangled grunter, Leiopotherapon unicolor). Waterholes on the main stems of rivers represent habitat for adult grunters. Grunters are sensitive to changes in flow regime – some critical requirements are flowing water and passage to spawning habitat (see Yang et al. (2024) for changes associated with instream structures) – and grunters are sensitive to cold-water pollution. The analysis considers change in flow regime and related habitat changes but does not consider the addition or loss of potential habitat associated with the creation of a dam impoundment or instream structures. Asset flow dependencies analysis Grunters were assessed across a total of 3948 km of river reaches in the Southern Gulf catchments with contributing flows from 62 model nodes (see Appendix A). Some of the key river reaches for grunter within the Assessment catchments were modelled downstream of nodes 9121074, 9121073 and 9129042. Locations were selected for modelling grunters based upon the species distribution model of the sooty grunter (Hephaestus fuliginosus) (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for grunters. When considering the mean weighted change in important flow metrics across all 62 grunter analysis nodes, the hypothetical dam scenarios ranged from negligible (0.2) to minor (4.5) for change in flow under BGPCT and B-D2, respectively. In contrast, water harvesting scenarios remained negligible with values ranging from 0.2 under Scenario BWT50P600R30E150 to 1.3 under Scenario B-WT150P200R30E0. Scenario CEdry resulted in a minor change (4.6) in important flow metrics for grunters. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for grunters (Figure 4-5). Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 30-day means of daily discharge at node 9121015. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean October discharge at node 9121015. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for grunters. Figure 4-5 Spatial heatmap of habitat-weighted changes in flow for grunters, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for grunters weighted by the habitat value of each reach. P907#yIS1 Water harvesting and changes in important flows for grunter The hypothetical water harvesting scenarios resulted in a mean negligible change across grunter assessment nodes from 0.2 to 1.3 under scenarios B-WT50P600R30E150 and B-WT150P200R30E0, respectively. The change in important flows for grunter under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-6). In both Scenario B-WT50P600R30E0 (with a low extraction target volume of 50 GL) and Scenario B-WT300P600R30E0 (with an extraction target volume of 300 GL), the mean weighted change in flows across the catchment was negligible (0.3). The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes to negligible values, ranging from 1.3 to 0.3, respectively (Figure 4-6). Grunters rely on specific flow regimes for critical life processes such as spawning, feeding and migration. Disruptions to these flows could have long-term ecological impacts. Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for grunter ecology. Figure 4-6 Habitat-weighted change in grunter flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for grunter. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P913#yIS1 Dams and changes in important flows for grunters Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.4) in important flow metrics when averaged across the 62 grunter assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for grunters was further reduced to 0.2. Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a minor (4.4) weighted change in important flows averaged across the assessment nodes. This was reduced to negligible (1.0) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a minor (4.5) mean change occurred across the catchment without transparent flows. This was reduced to minor (2.0) with provision of transparent flows (Scenario B-D2T). Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either single-dam scenario. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for grunters (Figure 4-6). Under dam scenarios, habitat-weighted changes in important flows for grunters were greatest at node 9121015 (Figure 4-6) with a change in important flows at this single node recorded as extreme (59.3). Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR resulted in moderate (5.8) and extreme (39.3) percentile change in important flows, respectively. These changes were reduced to negligible (0.1) and moderate (7.8) when modelled with transparent flows (scenarios B-DGPCT and B-DGRT, respectively). This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes. According to a study by Gehrke (1997), the abundance of sooty grunter in river reaches regulated by a single dam was significantly reduced. This decline is attributed to barriers to fish mobility and changes in sediment composition that alter habitats, though these effects are likely confined to areas directly downstream of the dam. Climate change and water resource development for important flows for grunters Scenario CEdry resulted in a minor change (4.6) in important flow metrics for grunters considering the mean across the 62 grunter assessment nodes (Figure 4-6). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.4) and B-WT150P600R30E0 (negligible; 0.3). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate (5.1) and minor (5.0) changes, respectively, when weighted across all grunter assessment nodes. This shows that the combined changes of scenarios D-DGPC or D-WT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160t200r30 alone. Overall grunters face four of the key threats related to flow modification: water harvesting, dam infrastructure, river regulation and the added threat of climate change. For Amniataba percoides, changes in flow regimes that lead to faster-flowing environments – for example, a dam structure that first holds back water then releases it at higher velocity – can lead to decreased population viability (Pusey et al., 2004). The key mechanisms for this are desynchronisation of thermal regimes and juvenile mortality caused by out-of-season high flows. In addition, dams impede access to spawning grounds (see Yang et al. (2024)). Species such as Leiopotherapon unicolor have habitat associations with riffle habitat (Keller et al., 2019), so any loss of this habitat by either reducing or increasing flows, or through inundation due to impoundment, would affect this species. 4.1.4 Mullet Mullet (a group including the genera Liza and Mugil) are fish that use marine habitats as adults to spawn and freshwater habitats as juveniles (a life history known as ‘catadromous’). Their life histories entail ‘catchment to coast’ habitats (i.e. freshwater, estuarine and marine) (Marin et al., 2003; Whitfield et al., 2012). Adults spawn in coastal habitats near river mouths, and small juveniles migrate upstream to freshwater habitats where they forage and grow (De Silva, 1980; Grant and Spain, 1975; Kailola et al., 1993; Robins et al., 2005). After about 4 years, they leave freshwater habitats and move to lower estuaries and the ocean. Mullet grow fastest during the tropical wet season in response to a seasonal increase in productivity of coastal waters (Grant and Spain, 1975; Whitfield et al., 2012). About 20 tropical mullet species occur in northern Australian waters from Townsville on the east coast to Broome in the west (Blaber et al., 2010). Mullet inhabit the estuarine and freshwater reaches of the rivers of the Southern Gulf catchments. Short-lived, fast growing and productive, mullet are important as a commercial, recreational and Indigenous fish resource. Mullet are one of the most important species taken in NT recreational catches and include the third most prominent species in (non-Indigenous) recreational catches in the east coast (Gulf of Carpentaria area) of the NT (West et al., 2012). Most of the NT recreational mullet catch (92.4%) is targeted (West et al., 2012) rather than bycatch. Mullet are of cultural significance for Indigenous communities throughout Australia and among the most numerous species in their catch (Henry and Lyle, 2003). In NT fisheries, mullet are a target for Aboriginal coastal fishing licences (Boyer, 2018; Wilton et al., 2018) and a target or bycatch in several fisheries (NT Government, 2022). The key threats to mullet are associated with the loss of riverine and overbank flood flows that reduce riverine–wetland connectivity and so reduce nutrient inputs and feeding opportunities. In addition, the loss of instream connectivity among deep-water pools due to reduced low-level flows would be a potential barrier to downstream movement of mullet to coastal waters. Asset flow dependencies analysis Mullets were assessed at the combined end-of-system node (9100000) in the Southern Gulf catchments (see Appendix A). Locations were selected for modelling mullet based upon habitat maps of key species (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for mullet. When considering the flow change in important flow metrics, the hypothetical dam scenarios ranged no measurable change (0) to a major change (21.7) under scenarios B-DGPCT and B-DGR, respectively. In contrast, water harvesting scenarios ranged from negligible (0.4) to moderate (8.9) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a major change (15.3) in important flow metrics for mullet (Figure 4-7). It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric low flood pulse count (<75th percentile) at node 9100000. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean winter discharge. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for mullet. Water harvesting and changes in important flows for mullet The hypothetical water harvesting scenarios resulted in a flow change of important metrics for mullet from negligible (0.4) to moderate (8.9) under scenarios B-WT50P600R30E250 and B- WT150P200R30E0, respectively, at the end-of-system node. The change in important flows for mullet under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-7). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the change in flows across the catchment was negligible (1), increasing to minor (2.3) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river flow level is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change from moderate (8.9) to negligible (1.9) (Figure 4-7). Measures to protect important components of the flow regime can support catchment ecology. For example, reducing the extraction target volume of water extracted in any water year, hence maintaining higher flood flow regimes, and increasing the pump-start threshold protects the low flows that are important for mullet ecology within riverine habitats. Figure 4-7 Change in mullet flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for mullet. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P932#yIS1 Dams and changes in important flows for mullet Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in no measurable change (0) in important flow metrics for mullet. There was little additional benefit when transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), as changes were minimal without transparent flows. Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a major (21.7) flow change in important flows. This was reduced to minor (3.8) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a major change (21.7) occurred without transparent flows. This was reduced to moderate (12.7) with provision of transparent flows (Scenario B-D2T). Scenario B-D2 (with two dams) resulted in a larger flow change than the single dam on Gunpowder Creek (scenario B-DGPC) but had a similar impact to a dam on the Gregory River (Scenario B-DGR). Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support the catadromous life history of mullet and their use of freshwater riverine habitats. Climate change and water resource development for important flows for mullet Scenario CEdry resulted in a major change (15.3) in important flow metrics for mullet considering the flow change. This indicates that the dry climate scenario led to a larger change in important flows than scenarios B-DGPC (no measurable change; 0) and B-WT150P600R30E0 (negligible; 1.9). Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in major change (15.3 and 16.3, respectively). This shows that the combined changes of scenarios D-DGPC or D-WT150P600R30E0 were higher than Scenario CEdry and the B-WT160t200r30 water harvest scenario alone, but not as high as the two-dam scenario (B-D2). Field studies have shown that juvenile and early-adult phase mullet prefer fresh and brackish waters, including palustrine wetlands which support optimal growth and survival (Cardona, 2000; Whitfield et al., 2012). Wetland ‘perimeter to area ratio’ and wetland ‘number of patches’ have been found to be strongly related to mullet catch, suggesting the extent and connectivity of estuarine habitats, intertidal and supra-littoral areas, and creeks and channels are important to mullet production (Meynecke et al., 2008). This is associated with the proxies of frequency and duration of high-flood events that support the inundation and availability of river floodplain, and estuarine supra-littoral habitats used prolifically by juvenile mullet during the wet season are important for mullet (O’Mara et al., 2021). Flooded wetland habitats are hot spots for primary productivity (Burford et al., 2016; Ndehedehe et al., 2020a; Ndehedehe et al., 2020b) and refugia for fish during the subsequent dry season (O’Mara et al., 2021). Reduced river flow volume and modified seasonality and volume of flows under water harvesting scenarios (e.g. B-WT150P200R30E0) and Scenario CEdry affect mullet negatively by reducing the extent and connectivity of estuarine and freshwater habitats. This links to growth and survival via lower seasonal food accessibility and non-optimal environmental conditions (Faggotter et al., 2013; Jardine et al., 2013; O’Mara et al., 2021) and by disrupting cues for spawning movements. Scenarios B-DGR and B-D2 cause major change to important flow dependencies for mullet populations via reductions of river flows and by reducing access to tributary catchments. Disrupting catchment connectivity by constructing instream barriers such as dams limits spatial access to freshwater habitats and restricts ontogenetic habitat selection that is crucial for the catadromous life history of mullet (Grant and Spain, 1975; O’Mara et al., 2021; Robins and Ye, 2007; Stuart and Mallen‐Cooper, 1999). Modified flows limit growth and survival via lower seasonal food accessibility and non-optimal environmental conditions (Faggotter et al., 2013; Jardine et al., 2013; O’Mara et al., 2021), and by disrupting cues for spawning movements (also see Yang et al. (2024) for changes associated with instream structures). 4.1.5 Sawfishes Four species of sawfish inhabit the Gulf of Carpentaria, and they are found in inshore marine habitats and estuaries. Tropical Australian waters are one of the last strongholds for sawfishes (Phillips et al., 2011). The two largest species, largetooth or freshwater sawfish (Pristis pristis) and green sawfish (P. zijsron) are listed as Critically endangered on the IUCN Red List of Threatened Species and Vulnerable under the Commonwealth EPBC Act. The dwarf sawfish (P. clavata) is listed as Critically endangered (IUCN) and Vulnerable (EPBC Act), while the narrow sawfish (Anoxypristis cuspidata) is listed as Critically endangered (IUCN) and not listed under the EPBC Act. Freshwater sawfish is found in riverine reaches during the juvenile phase, after which it moves to coastal and marine habitats as adults. During research surveys, juvenile dwarf sawfish have been caught in upper estuary and lower riverine reaches in relatively pristine tropical Australian rivers, while adult dwarf sawfish are regularly caught as part of offshore fishing operations (Fry et al., 2021; Morgan et al., 2020). The green sawfish is common in estuaries and on occasion is also found in riverine habitats across northern Australia (Morgan et al., 2017a; Wueringer et al., 2023). In the Southern Gulf catchments, sawfishes occupy estuarine and freshwater reaches, and they also live in offshore Gulf of Carpentaria habitats. In northern Australia, all sawfish species pup in estuarine and inshore waters, and estuarine and riverine connectivity is critical for the survival of freshwater sawfish (Dulvy et al., 2016; Morgan et al., 2017b). Sawfishes are important for Indigenous Peoples in northern Australia, both culturally (Ebner et al., 2016; Finn and Jackson, 2011; Jackson et al., 2011) and as a food source (Naughton et al., 1986). In Australia, only Indigenous Australians are allowed to capture sawfishes. Freshwater sawfish, in particular, are affected by variability in the flow regime despite sustained riverine and estuarine connectivity during the wet season. Strong upstream recruitment of juveniles to riverine habitats only occurs during the highest flood flows (Lear et al., 2019). The higher the volume of flood flows, the greater the sustained body condition of sawfish during the subsequent dry season (Lear et al., 2021). The key threats to sawfishes are associated with the loss of high-level flood flows to support upstream recruitment and with any reduction in low-level dry-season flows that would reduce instream connectivity or create barriers among deep-water pools and reduce their persistence or water quality during the dry season. The analysis considers change in flow regime and related habitat changes but does not consider the loss of potential habitat associated with the creation of a dam impoundment or instream structures (see also Yang et al. (2024) for dam impoundments). Asset flow dependencies analysis Sawfishes were assessed across a total of 3948 km of river reaches in the Southern Gulf catchments and in the marine region with contributing flows from 63 model nodes (see Appendix A). Some of the key river reaches for sawfish within the assessment catchments were modelled downstream of nodes 9129042 and 9121053 and at the combined end-of-system node 9100000. Locations were selected for modelling sawfish based upon the species distribution models of freshwater or largetooth sawfish (Pristis pristis) (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for sawfish. When considering the mean weighted change in important flow metrics across all 63 sawfish analysis nodes, the change in flow ranged from negligible (0.6) to moderate (5.2) under the hypothetical dam scenarios B-DGPCT and B-D2, respectively. In contrast, change in flow under water harvesting scenarios remained negligible, with values of 0.5 under Scenario B-WT50P600R30E250 to 1.4 under Scenario B-WT150P200R30E0. Scenario CEdry resulted in a moderate change (6.5) in important flow metrics for sawfish. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for sawfish (Figure 4-8). Under scenario D2, the largest contributing change in important flow dependencies was for the metric low flood pulse count (<75th percentile) at node 9121012. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean November discharge at node 9121012. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for sawfishes. Figure 4-8 Spatial heatmap of habitat-weighted changes in flow for sawfish, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for sawfish weighted by the habitat value of each reach. P951#yIS1 Water harvesting and changes in important flows for sawfish The hypothetical water harvesting scenarios resulted in negligible mean change across sawfish assessment nodes ranging from 0.5 under Scenario B-WT50P600R30E250 to 1.4 under Scenario B- WT150P200R30E0. The change in important flows for sawfish under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-9). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the mean weighted change in flows across the catchment was negligible (0.5). It increased to 0.9 (negligible) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes from 1.4 to 0.7, respectively (both negligible) (Figure 4-9). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for sawfish ecology. Flow modifications, particularly the reduction of high flows and shortened duration of the peak water levels, can affect species such as the sawfish that rely on floodplain inundation and wetland connectivity (modelled in Section 4.4.10). Furthermore, the maintenance of depth and persistence of important riverine pools during the dry season may be reduced by water impoundment or upstream extraction (see Section 4.4.2 for refuge waterholes). Figure 4-9 Habitat-weighted change in sawfish flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for sawfish. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P956#yIS1 Dams and changes in important flows for sawfish Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.7) in important flow metrics when averaged across the 63 sawfish assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for sawfish was reduced to negligible (0.6). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a minor (4.9) weighted change in important flows averaged across the assessment nodes. This was reduced to negligible (1.6) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a moderate (5.2) mean change occurred across the catchment without transparent flows. This was reduced to minor (3.1) with provision of transparent flows (Scenario B-D2T). Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either of the single-dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing transparent flows to support environmental outcomes for sawfish. Under dam scenarios, habitat-weighted changes in important flows for sawfish were greatest at node 9121012 (Figure 4-9), with a change in important flows at this single node recorded as extreme (48.2). Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR experienced significant changes in important flows, with major (16.3) and extreme (45.4) percentile shifts, respectively. When modelled with transparent flows, these changes were mitigated to moderate levels with shifts of 11.7 and 12.9, respectively. This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes. The location of single dams further up in the catchment likely reduces the level of potential impact on sawfish; however, sawfish are likely to be affected by a combination of impacts associated with water resource development beyond changes in flow and loss of connectivity. Climate change and water resource development for important flows for sawfish Scenario CEdry resulted in a moderate change (6.5) in important flow metrics for sawfish considering the mean across the 63 sawfish assessment nodes (Figure 4-9). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.7) and B-WT150P600R30E0 (negligible; 0.7). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in moderate change (7.1 and 7, respectively) when weighted across all sawfish assessment nodes. This shows that the changes under scenarios D-DGPC or D-WT150P600R30E0 were higher than under Scenario CEdry or either of scenarios B-D2 and B-WT160t200r30 alone, and they would have moderate change to freshwater sawfish flow dependencies via flow reduction, particularly if it was to affect wet-season flood flows and late-dry-season low flows that result from storm rains. Reduction of wet-season high flows due to two dams or water extraction would reduce the potential for sawfish neonate recruitment upstream and connectivity to wetland habitats. In addition, dams impede access to juvenile riverine habitats (see Yang et al. (2024)). Research has shown that recruitment and body condition, growth and survival of largetooth sawfish within riverine freshwater habitats are critically dependent on large flood flows (e.g. the 98th percentile of recorded flows) (Lear et al., 2019) and persistent, extensive riverine pools that act as critical refugia for sawfish during the following dry season (Lear et al., 2021). Modified flows can reduce sawfish neonate recruitment (Morgan et al., 2016), affect the potential growth of individuals (Hunt et al., 2012), reduce the abundance of sawfish prey species that use floodplain wetlands during their life cycle (Novak et al., 2017), and reduce sawfish abundance and survivorship (Close et al., 2014; Jellyman et al., 2016; Morgan et al., 2016). Water resource development and a drying climate have been modelled to have significant negative impacts on sawfish populations in Australian tropical rivers (Plagányi et al., 2024). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for sawfish ecology. Flow modifications, particularly the reduction of high flows and shortened duration of the peak water levels, can affect species such as the sawfish that rely on floodplain inundation and wetland connectivity (modelled in Section 4.4.10). Furthermore, the maintenance of depth and persistence of important riverine pools during the dry season may be reduced by water impoundment or upstream extraction (see Section 4.4.2 for refuge waterholes). 4.1.6 Threadfin King threadfin (Polydactylus macrochir, formerly P. sheridani) is a large (>1.5 m) marine carnivorous fish in the order Perciformes. Endemic to Australasia, king threadfin range from the Exmouth Gulf, Western Australia, across northern Australia and southern Papua New Guinea to the Brisbane River in Queensland (Motomura et al., 2000). In the Southern Gulf catchments, king threadfin occupy relatively pristine habitats in estuarine reaches as well as coastal marine waters. They are not found in freshwater habitats (Blaber et al., 1995; Moore et al., 2012). King threadfin are long-lived (22 years) and fast growing. They begin life as males but change to females as they age (protandrous hermaphrodites). They mate and spawn in the lower estuary during the dry season to early wet season. King threadfin use both visual and tactile cues as predators. They benefit from turbid waters during wet-season flows, as they can successfully forage for prey while turbidity protects young threadfin from large predators (Welch et al., 2014). As a prized table fish, king threadfin are a target species for recreational and Indigenous fisheries throughout wet-dry tropical Australia (Moore et al., 2011). They typically are the second-most- important target species in the commercial, inshore gill net fisheries that principally target barramundi (Welch et al., 2010). In 2018–19, 235 t of king and blue (Eleutheronema tetradactylum) threadfin worth $923,000 were taken in the NT. King threadfin are of cultural significance for the Indigenous community, and in key localities in the vicinity of Indigenous townships in the NT they are subject to management plans specifying season and bag limits (Malak Malak: Land and Water Management, 2016). The key threats to threadfin are associated with the loss of estuarine overbank flood flows and consequent reduction of salt flat inundation and ephemeral habitat for foraging threadfin. Also, infrequent inundation would reduce nutrient inputs to estuaries and thus affect the habitat of threadfin prey populations that are subsequently available to the threadfin within the estuarine habitat. In addition, the loss of instream connectivity among deep-water pools due to reduced low-level flows would be a potential barrier to downstream movement of threadfin’s prey to coastal waters. Asset flow dependencies analysis Threadfin were assessed at the combined end-of-system node (9100000) in the near-shore zone of the Southern Gulf catchments (see Appendix A). Locations were selected for modelling threadfin based upon habitat maps of king threadfin (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for threadfin. When considering the change in important flow metrics, the changes under the hypothetical dam scenarios ranged from no measurable change (0) under Scenario B-DGPCT to a major change (20.1) under Scenario B-DGR. In contrast, changes under water harvesting scenarios ranged from negligible (0.4) to moderate (8.9) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (14.8) in important flow metrics for threadfin. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 3-day means of daily discharge. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean November discharge. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for threadfin. Water harvesting and changes in important flows for threadfin The hypothetical water harvesting scenarios resulted in a flow change for threadfin from negligible (0.4) to moderate (8.9) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. The change in important flows for threadfin under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-10). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the flow change in important metrics was negligible (0.9). It increased to minor (2.4) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river flow level is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the flow-induced change across the assessment nodes from moderate (8.9) to negligible (1.9) (Figure 4-10). Measures to protect important parts of the catchment flow regime can support catchment-wide ecology. For example, reducing the extraction target volume of water extracted in any water year, maintaining annual freshwater inputs to the estuary, and increasing the pump- start threshold protects the early-season low flows that are important to invigorate preferred brackish threadfin estuarine habitats at the end of the dry season. Figure 4-10 Change in threadfin flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for threadfin. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P977#yIS1 Dams and changes in important flows for threadfin Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in no measurable change (0) in important flow metrics at the end-of-system node, hence there was no additional improvement when transparent flows (Scenario B-DGPCT) were implemented. Scenario B-DGR resulted in a larger change than Scenario B-DGPC with a major (20.1) weighted change in important flows averaged across the assessment nodes. This was reduced to minor (4) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a major change (20.1) occurred without transparent flows. This was reduced to moderate (14.4) with provision of transparent flows (Scenario B-D2T). Scenario B-D2 (with two dams) did not result in a larger flow change than a single dam on the Gregory River (scenario B-DGR). Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for threadfin in downstream estuarine habitats, particularly enhancing brackish ecotones which are supported by seasonal freshwater flows. Climate change and water resource development for important flows for threadfin Scenario CEdry resulted in a moderate change (14.8) in important flow metrics for threadfin considering flows at the end-of-system node (Figure 4-10). This indicates that the dry climate scenario led to a larger change than scenarios B-DGPC (negligible; 0) and B-WT150P600R30E0 (negligible; 1.9). Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate (14.8) and major (16) change, respectively. This shows that the combined changes of scenarios D-DGPC or D-WT150P600R30E0 were similar (in the case of a dam on Gunpowder Creek) or higher (for water extraction) than Scenario CEdry or either of scenarios B-D2 and B-WT160t200r30 alone. King threadfin do not use freshwater reaches of rivers as habitat. However, both recruitment and survival of juvenile king threadfin were found to be positively related to the annual levels of freshwater flow during spring and summer in a large Queensland subtropical estuary (Halliday et al., 2008). In the Gulf of Carpentaria, the survival and growth of king threadfin is likely supported by higher estuarine productivity and abundant prey in years of high flood flow (Halliday et al., 2012; Moore et al., 2012). Carbon and nutrients are exported to the estuarine and near-shore habitats where they support the food chain and the prey of king threadfin. In Australian tropical rivers, both commercial catch (as a measure of abundance) and year-class strength were positively related to monsoon rainfall (often year-lagged) and the resulting changes in flow in some rivers, but not for all aspects of river flow (Halliday et al., 2012; Welch et al., 2014). The hypothetical construction of a dam on the Gregory River would have a major impact on king threadfin flow dependencies under the Southern Gulf catchments scenarios, while water harvesting with a low pump threshold and no annual diversion commencement flow requirement results in a moderate change to flow dependencies for the species. Under water harvesting scenarios that vary by extraction volume and pump threshold (e.g. scenarios B-WT300P600R30E0 and B-WT150P200R30E0), minor to moderate change in flows occurred in association with reduced natural flow volumes, particularly interrupted flows in the late dry season. Changes in seasonality of flows would reduce the growth and abundance of king threadfin, as has been found for other large predatory fish that use Gulf of Carpentaria estuaries as prime habitat (Leahy and Robins, 2021). The brackish estuarine ecotone is prime habitat for threadfin prey (Cardona, 2000; Russell and Garrett, 1983; Vance et al., 1998), and the decrease in wet-season flow volumes would reduce the extent and persistence of the brackish ecotone and hence prey abundance. In addition, low-level flows in the spring and late dry season are used by threadfin larvae in marine habitats as cues to access estuarine habitats. Under a future dry climate, and particularly in combination with water resource development, moderate to major impacts on threadfin via river flow reduction would exacerbate the suite of impacts on threadfin. 4.2 Waterbirds The waterbird groups are colonial and semi-colonial nesting waders; shorebirds; cryptic waders; and swimming, diving and grazing waterbirds. These groups are based on waterbird foraging behaviour and habitat dependencies, together with nesting behaviour and habitat dependencies. Both foraging and nesting dependencies need to be taken into account, because while some species both forage and nest in northern Australia, others migrate annually to take advantage of foraging opportunities and avoid the northern hemisphere winter. 4.2.1 Colonial and semi-colonial wading waterbirds The colonial and semi-colonial wading waterbirds (‘colonial waders’) group comprises 21 species from five families, including black-necked stork, brolgas, herons, egrets, ibises and spoonbills (Appendix E). Changes in the depth, extent and duration of inundation in shallow wetland habitats used by colonial waders for nesting and foraging can have significant impacts on nesting, nest success, juvenile recruitment and adult survival. Because of the specific needs of colonial waders in terms of water regimes in suitable nesting habitats, colony sites in areas subject to changes in flood regimes due to water resource developments (e.g. river regulation through dams and weirs, water extraction from rivers, floodplain water harvesting) or climate change are at high risk of damage or loss, which has implications for population maintenance. The analysis considers change in flow regime and related habitat changes but does not consider the addition of potential habitat associated with the creation of a dam impoundment (see also Yang et al. (2024) for dam impoundments). Asset flow dependencies analysis Colonial waders were assessed across a total of 3948 km of river reaches in the Southern Gulf catchments with contributing flows from 62 model nodes (see Appendix A). Some of the key river reaches for colonial waders within the Assessment catchments were modelled downstream of nodes 9121010, 9130010 and 9130111. Locations were selected for modelling colonial waders based upon the species distribution models of royal spoonbill (Platalea regia) (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for colonial waders. When considering the mean weighted change in important flow metrics across all 62 colonial wader analysis nodes, the change in flow under hypothetical dam scenarios ranged from negligible (1.6) under Scenario B-DGPCT to minor (3.7) under Scenario B-D2T. In contrast, the change in flow under water harvesting scenarios ranged from negligible (1.4) under Scenario B-WT50P600R30E0 to minor (2.6) under Scenario B-WT300P600R30E0. Scenario CEdry resulted in a moderate change (11.4) in important flow metrics for colonial waders. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for colonial waders (Figure 4-11). Under scenario D2, the largest contributing change in important flow dependencies was for the metric high flow pulse duration single spell (10th percentile) at node 9130030. For scenario B-WT150P200R30E0, the largest contribution of change was also for the same metric but at node 9130061. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for colonial and semi-colonial wading waterbirds. Figure 4-11 Spatial heatmap of habitat-weighted changes in flow for colonial and semi-colonial wading waterbirds, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for colonial waders weighted by the habitat value of each reach. P995#yIS1 Water harvesting and changes in important flows for colonial and semi-colonial waders Water harvesting scenarios also affected the flow metrics important to colonial waders. The hypothetical water harvesting scenarios resulted in a mean change across colonial and semi- colonial waders assessment nodes from negligible (1.4) to minor (2.6) under scenarios B-WT50P600R30E0 and B-WT300P600R30E0, respectively. The change in important flows for colonial and semi-colonial waders under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-12). Under Scenario B-WT50P600R30E0 with a low extraction target volume of 50 GL, the mean weighted change in flows across the catchment was negligible (1.4). This increased to minor (2.6) with a higher extraction target volume of 300 GL under Scenario B-WT300P600R30E0. With a target extraction volume of 150 GL, increasing the pump-start threshold, which protects low flows, from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) maintained the change across assessment nodes as negligible (1.9). Such measures are critical in protecting important parts of the flow regime and thereby supporting colonial and semi-colonial wader ecology. Figure 4-12 Habitat-weighted change in colonial and semi-colonial wading waterbird flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for colonial and semi-colonial wading waterbirds. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1000#yIS1 Dams and changes in important flows for colonial waders Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (1.8) in important flow metrics when averaged across the 62 colonial wader assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for colonial waders was reduced to negligible (1.6). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a minor (2.9) weighted change in important flows averaged across the assessment nodes. This increased to 3.1 with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a minor (3.7) mean change occurred across the catchment without and without transparent flows, suggesting that their impact in multi-dam scenarios may be less pronounced. Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either of the single-dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, showing the importance of maintaining flows for supporting colonial and semi-colonial waders’ habitats Figure 4-12). While transparent flows are beneficial in mitigating important flow change in single-dam scenarios, their effectiveness seems limited when two dams are involved. Dams play a significant role in altering flood regimes, reducing the extent, frequency, and depth of floods essential for colonial waders’ breeding environments. Such alterations can lead to long-term abandonment of breeding sites due to increased nest failure and predation risks, and prolonged intervals between necessary inundation events can threaten population maintenance (Bino et al., 2014; Brandis et al., 2018; Brandis et al., 2011; Kingsford et al., 2011). Under dam scenarios, habitat-weighted changes in important flows for colonial waders were greatest at node 9130030 (Figure 4-12) with an extreme change (36.1) recorded at this single node. Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR resulted in extreme (36.1) and major (15.2) percentile change in important flows, respectively. These changes were reduced to major (28.1 and 16.8, respectively) when modelled with transparent flows. Climate change and water resource development for important flows for colonial and semi- colonial waders Under Scenario CEdry, the impact on colonial waders resulted in a moderate change (11.4) in important flow metrics for colonial and semi-colonial waders considering the mean across the 62 colonial wader assessment nodes (Figure 4-12). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 1.8) and B-WT150P600R30E0 (negligible; 1.9). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate change (both 12.4) when weighted across all colonial wader assessment nodes. This shows that the combined changes under scenarios D-DGPC or D-WT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160P200R30 alone. Considering downstream flow regime change, colonial and semi-colonial waders are sensitive to changes in the depth, extent and duration of shallow wetland environments, particularly during nesting events (see Section 4.4.1 for wetland habitats). Completion of a full nesting cycle can take several months. During this time, changes in water depth, water extent, water duration or food availability can force adults to abandon their nests or expose nests to predation, resulting in nest failure. In the long term, this can result in abandonment of regular breeding sites (Brandis, 2010; Brandis et al., 2011). Breeding sites in areas subject to changes in flood regimes are at high risk of damage or loss, with implications for population maintenance (Bino et al., 2014; Brandis et al., 2018; Brandis et al., 2011; Kingsford et al., 2011). Changes can occur when flood peaks are reduced by water extraction or dams (e.g. by reducing flood extent, frequency, duration or depth), when floodwater is captured on floodplains (e.g. by dams, levees or roads) or when the time between the inundation events that create these habitats is extended (Kingsford and Thomas, 2004). 4.2.2 Cryptic wading waterbirds The cryptic waders group comprises wading waterbird species that are relatively difficult to detect and have a high level of dependence on shallow temporary and permanent wetland habitats with relatively dense emergent aquatic vegetation (Marchant and Higgins, 1990). Their habitats (e.g. reeds, rushes, sedges, wet grasses) require regular or ongoing inundation to survive. In northern Australia, this group comprises 13 species from four families, including bitterns, crakes, rails and snipes (Appendix E). Cryptic waders are found throughout the Southern Gulf catchments. Many species have been recorded at Lake Moondarra, a permanent lake with fringing wetlands, near Mount Isa. Australian painted snipe (Rostratula australis), Australian spotted crake (Porzana fluminea), Latham’s snipe (Gallinago hardwickii), and to a lesser extent, the black bittern (Ixobrychus flavicollis) have been recorded in this area (Atlas of Living Australia, 2023a; 2023b). Black bittern has also been recorded at Lawn Hill Gorge (Department of Agriculture‚ Water and the Environment, 2021a). Striated heron (Butorides striatus), chestnut rail (Eulabeornis castaneoventris) and black bittern have also been recorded at the coastal wetland aggregations (Atlas of Living Australia, 2023a; 2023b). The cryptic waders’ need for appropriate vegetation and shallow-water environments makes them sensitive to changes in both water regimes and vegetation throughout their life cycles. Thus, the primary pathway of potential water resource development impact on cryptic waders is habitat loss, fragmentation and change caused by changes in the timing, extent, depth and duration of inundation, which in turn changes vegetation. The analysis considers change in flow regime and related habitat changes but does not consider the addition of potential habitat associated with the creation of a dam impoundment (see also Yang et al. (2024) for dam impoundments). Asset flow dependencies analysis Cryptic wading waterbirds were assessed across a total of 2027 km of river reaches in the Southern Gulf catchments with contributing flows from 34 model nodes (see Appendix A). Some of the key river reaches for cryptic wading waterbirds within the Assessment catchments were modelled downstream of nodes 9139000, 9130140 and 9130050. Locations were selected for modelling cryptic wading waterbirds based upon species distribution models of the Australian painted snipe (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for cryptic wading waterbirds. When considering the mean weighted change in important flow metrics across all 34 cryptic wading waterbird analysis nodes, the hypothetical dam scenarios ranged from negligible (0.2) to moderate (8.5) for change in flow under scenarios B-DGPCT and B-D2, respectively. In contrast, water harvesting scenarios ranged from negligible (0.3) to minor (2.1) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (12.5) in important flow metrics for cryptic wading waterbirds. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for cryptic wading waterbirds (Figure 4-13). Under scenario D2, the largest contributing change in important flow dependencies was for the metric low flood pulse count (<75th percentile) at node 9121050. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric high flood pulse count (25th percentile) at node 9121052. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for cryptic wading waterbirds. Figure 4-13 Spatial heatmap of habitat-weighted changes in flow for cryptic wading waterbirds, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for cryptic wading waterbirds weighted by the habitat value of each reach. P1016#yIS1 Water harvesting and changes in important flows for cryptic wading waterbirds The hypothetical water harvesting scenarios resulted in a mean change across cryptic wading waterbirds assessment nodes from negligible (0.3) to minor (2.1) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. The change in important flows for cryptic wading waterbirds under water harvesting scenarios varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-14). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the mean weighted change in flows across the catchment was negligible (0.3). It increased to 0.9 with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes from minor (2.1) to negligible (0.6) (Figure 4-14). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for cryptic wading waterbird ecology. Figure 4-14 Habitat-weighted change in cryptic wading waterbird flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for cryptic wading waterbird. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1021#yIS1 Dams and changes in important flows for cryptic wading waterbirds Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.3) in important flow metrics when averaged across the 34 cryptic wading waterbird assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for cryptic wading waterbirds was reduced further to 0.2. Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a moderate (8.3) weighted change in important flows averaged across the assessment nodes. This was reduced to negligible (2.0) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a moderate (8.5) mean change occurred across the catchment without transparent flows. This was reduced to minor (4.2) with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either of the single-dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for cryptic wading waterbirds (Figure 4-14). Under dam scenarios, habitat-weighted changes in important flows for cryptic wading waterbirds were greatest at node 9121050 (Figure 4-14) with a change in important flows at this single node recorded as extreme (61.2). Nodes directly downstream of the dam in Scenario B-DGR resulted in extreme (61.2) percentile change in important flows. These changes were reduced to major (15.5) when modelled with transparent flows. This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes. Cryptic wading waterbirds, a group sensitive to changes in shallow wetland environments and the fringes of deeper water habitats, are particularly vulnerable under these scenarios. These species typically nest on the ground or in low vegetation, making them susceptible to fluctuations in water levels caused by dam operations. Such changes can alter foraging, nesting, and refuge habitats, degrade water quality, reduce food availability, and increase competition, predation and disease (Kingsford and Norman, 2002; Marchant and Higgins, 1990; McGinness, 2016). The extreme changes observed at certain nodes highlight the potential for significant ecological disruption in areas directly affected by dam operations, especially if transparent flows are not implemented. Climate change and water resource development for important flows for cryptic wading waterbirds Scenario CEdry resulted in a moderate change (12.5) in important flow metrics for cryptic wading waterbirds considering the mean across the 34 cryptic wading waterbird assessment nodes (Figure 4-14). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.3) and B-WT150P600R30E0 (negligible; 0.6). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in moderate change (12.7 and 12.9, respectively) when weighted across all cryptic wading waterbirds assessment nodes. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160P200R30 alone. Considering the sensitivity of cryptic waders to changes in their habitat, particularly during nesting events, the observed changes in flow metrics under these scenarios underscore the importance of implementing measures to protect and manage wetland environments. Changes in water depth, extent, and duration, or disruptions caused by climate change and water resource development, can force these birds to abandon nests or lead to habitat loss, with long-term implications for their populations (Brandis et al., 2009; Kingsford and Norman, 2002). Climate change and climate- change-driven extremes are likely to interact with changes induced by water resource development, including inundation of freshwater habitats by seawater and inundation of nests by extreme flood events or seawater intrusion. 4.2.3 Shorebirds The shorebirds group consists of waterbirds with a high level of dependence on end-of-system flows and large inland flood events that provide broad areas of shallow-water and mudflat environments (see Appendix E for species list). Shorebirds are largely migratory and mostly breed in the northern hemisphere (Piersma and Baker, 2000). They are in significant decline and are of international concern (Clemens et al., 2010; Clemens et al., 2016; Nebel et al., 2008). Shorebirds depend on specific shallow-water habitats in distinct geographic areas, including northern hemisphere breeding grounds, southern hemisphere non-breeding grounds, and stopover sites along migration routes such as the East Asian-Australasian Flyway (Bamford, 1992; Hansen et al., 2016). In northern Australia, this group comprises approximately 55 species from four families, including sandpipers, godwits, curlew, stints, plovers, dotterel, lapwings and pratincoles. Approximately 35 species are common regular visitors or residents. Several species in this group are endangered globally and nationally, including the bar-tailed godwit (Limosa lapponica), curlew sandpiper (Calidris ferruginea), eastern curlew (Numenius madagascariensis), great knot (Calidris tenuirostris), lesser sand plover (Charadrius mongolus) and red knot (Calidris canutus). An example species from this group is the eastern curlew which is listed as Critically endangered under the EPBC Act and is recognised through multiple international agreements as requiring habitat protection in Australia. Eastern curlews rely on food sources along shorelines, mudflats and rocky inlets and also need roosting vegetation (Driscoll and Ueta, 2002; Finn et al., 2007; Finn and Catterall, 2022). Developments and disturbances such as recreational, residential and industrial use of these habitats have restricted habitat and food availability for the eastern curlew, contributing to population declines. Shorebirds are found throughout the Southern Gulf catchments (see Merrin et al. (2024)). The most common species are Australian pratincole (Stiltia isabella), black-fronted dotterel (Elseyornis melanops), masked lapwing (Vanellus miles), red-kneed dotterel (Erythrogonys cinctus) and sharp- tailed sandpiper (Calidris acuminata) (Atlas of Living Australia, 2023a; 2023b). The intertidal mudflats and coastal flats (see also Section 4.4.4) provide important habitat for shorebirds, as do the large open shallow wetlands (Chatto, 2006). Shorebirds rely on the inundation of shallow flat areas such as mudflats and sandflats during seasonal high-level flows to provide invertebrates and other food sources. Without inundation events, these habitats cannot support high densities of shorebird species, and lack of food can increase mortality rates both on-site and during and after migrations (Barbaree et al., 2020; Canham et al., 2021; Durrell, 2000; Kozik et al., 2022; van der Pol, et al., 2024; West et al., 2005). The analysis considers change in flow regime and related habitat changes but does not consider the addition or loss of potential habitat associated with the creation of a dam impoundment (see also Yang et al. (2024) for dam impoundments). Asset flow dependencies analysis Shorebirds were assessed across a total of 3948 km of river reaches in the Southern Gulf catchments with contributing flows from 62 model nodes (see Appendix A). Some of the key river reaches for shorebirds within the Assessment catchments were modelled downstream of nodes 9129042, 9121016 and 9121015. Locations were selected for modelling shorebirds based upon the species distribution models of the eastern curlew (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for shorebirds. When considering the mean weighted change in important flow metrics across all 62 shorebirds analysis nodes, the hypothetical dam scenarios remained negligible (0.4 to 1.8) for change in flow under scenarios B-DGPCT and B-D2, respectively. Similarly, water harvesting scenarios also remained negligible (0.3 to 0.8) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a minor change (3.6) in important flow metrics for shorebirds. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for shorebirds (Figure 4-15). Under scenario D2, the largest contributing change in important flow dependencies was for the metric fall rate – the mean rate of negative changes in flow from one day to the next at node 9121015. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean winter discharge at node 9121015. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow- ecology relationships for shorebirds. Figure 4-15 Spatial heatmap of habitat-weighted changes in flow for shorebirds, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for shorebirds weighted by the habitat value of each reach. P1038#yIS1 Water harvesting and changes in important flows for shorebirds The hypothetical water harvesting scenarios resulted in a mean change across shorebirds assessment nodes from a negligible (0.3) in Scenario B-WT50P600R30E250 to a slightly higher but still negligible (0.8) in Scenario B-WT150P200R30E0. The magnitude of these changes varied depending on factors such as extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-16). Under Scenario B-WT50P600R30E0, with a low extraction target volume of 50 GL, the mean weighted change in flows across the catchment was negligible (0.4). It remained negligible (0.6) at an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes from negligible 0.8 to 0.5 (Figure 4-16). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for shorebirds ecology. Figure 4-16 Habitat-weighted change in shorebird flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for shorebirds. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1043#yIS1 Dams and changes in important flows for shorebirds Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.4) in important flow metrics when averaged across the 62 shorebird assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for shorebirds remained negligible (0.4). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, also with a negligible (1.7) weighted change in important flows across the assessment nodes. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a negligible (1.8) mean change occurred across the catchment without transparent flows. Transparent flows maintained these dam scenarios at negligible levels of change in important flow dependencies. Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either of the single- dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for shorebirds (Figure 4-15). Under dam scenarios, habitat-weighted changes in important flows for shorebirds were greatest at node 9121015 (Figure 4-16) with an extreme change (33.7) recorded at this single node. Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR resulted in moderate (5.6) and major (15.5) percentile change, respectively, in important flows. These changes were reduced to minor (4.2) and moderate (5.9), respectively, when modelled with transparent flows. This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes (Figure 4-16). Given the shorebirds’ sensitivity to changes in their preferred habitats, the extreme changes observed at certain nodes underscore the potential for significant ecological disruption under dam scenarios, particularly without transparent flows. Climate change and water resource development for important flows for shorebirds Scenario CEdry resulted in a minor change (3.6) in important flow metrics for shorebirds considering the mean across the 62 shorebird assessment nodes (Figure 4-16). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.4) and B-WT150P600R30E0 (negligible; 0.5). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in minor change (3.8 and 3.9, respectively) when weighted across all shorebird assessment nodes. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160P200R30 alone. Waterbird species in the shorebirds group are sensitive to changes in the depth, extent and duration of inundation of open very shallow water environments, including the edges of inland floodplains and lakes, and estuarine and coastal mudflats and sandflats (Albanese and Davis, 2015; Donnelly et al., 2020; Fernandez and Lank, 2008; Ge et al., 2009; Jackson et al., 2019; Schaffer- Smith et al., 2017). Their preference for open flat areas and good visibility when foraging means that encroachment of dense vegetation or human activity can prevent their use of a site (Baudains and Lloyd, 2007; Ge et al., 2009; Tarr et al., 2010). Shorebirds rely on the inundation of shallow flat areas such as mudflats and sandflats to provide invertebrates and other food sources (Aharon- Rotman et al., 2017; Galbraith et al., 2002). Without inundation events, these habitats cannot support high densities of shorebird species, and lack of food can increase mortality rates both on- site and during and after migrations (Aharon-Rotman et al., 2017; Goss-Custard, 1977; Rushing et al., 2016). Climate change, as indicated by Scenario CEdry, is likely to affect habitat availability and quality among other factors for shorebirds, including changing freshwater inflows and the availability of mudflats and similar environments (Bellisario et al., 2014; Iwamura et al., 2013). 4.2.4 Swimming, diving and grazing waterbirds The swimming, diving and grazing waterbirds group comprises species with a relatively high level of dependence on semi-open, open and deeper water environments, who commonly swim when foraging (including diving, filtering, dabbling, grazing) or when taking refuge (see Appendix E for species list). In northern Australia, this group comprises 49 species from 11 families, including ducks, geese, swans, grebes, pelicans, darters, cormorants, shags, swamphens, gulls, terns, noddies and jacanas. The most common species are Australasian darter (Anhinga novaehollandiae), Australasian grebe (Tachybaptus novaehollandiae), Australian pelican (Pelecanus conspicillatus), grey teal (Anas gracilis), hardhead (Aythya australis), little black cormorant (Phalacrocorax sulcirostris) and little pied cormorant (Microcarbo melanoleucos) (Atlas of Living Australia, 2023a; 2023b). Reduced extent, depth and duration of inundation of waterhole and other deep-water environments are likely to reduce habitat availability and food availability for swimming, diving and grazing waterbirds. Reduced high-level flows increases competition, and predation also increases the risk of disease and parasite spread. Conversely, species in this group that nest at water level or just above, such as the magpie goose (Anseranas semipalmata), are particularly at risk of nests drowning when water depths increase unexpectedly. The analysis considers change in flow regime and related habitat changes but does not consider the addition or loss of potential habitat associated with the creation of a dam impoundment (see also Yang et al. (2024)). Asset flow dependencies analysis Swimming, diving and grazing waterbirds were assessed across a total of 3948 km of river reaches in the Southern Gulf catchments with contributing flows from 62 model nodes (see Appendix A). Some of the key river reaches for swimming, diving and grazing waterbirds within the Assessment catchments were modelled downstream of nodes 9130070, 9130111 and 9121161. Locations were selected for modelling swimming, diving and grazing waterbirds based upon the species distribution models of the magpie goose (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for swimming, diving and grazing waterbirds. When considering the mean weighted change in important flow metrics across all 62 swimming, diving and grazing waterbirds analysis nodes, the hypothetical dam scenarios ranged from negligible (0.5) to minor (3.5) for change in flow under scenarios B-DGPCT and B-D2, respectively. In contrast, water harvesting scenario change values were negligible, ranging from 0.4 to 1.1 under scenarios B-WT50P600R30E150 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in moderate change (5.1) in important flow metrics for swimming, diving and grazing waterbirds. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for swimming, diving and grazing waterbirds (Figure 4-17). Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 30-day means of daily discharge at node 9121015. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric high flow pulse duration (25th percentile) at node 9121015. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for swimming, diving and grazing waterbirds. Figure 4-17 Spatial heatmap of habitat-weighted changes in flow for swimming, diving and grazing waterbirds, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for swimming, diving and grazing waterbirds weighted by the habitat value of each reach. P1059#yIS1 Water harvesting and changes in important flows for swimming, diving and grazing waterbirds The hypothetical water harvesting scenarios resulted in negligible mean change across swimming, diving and grazing waterbirds assessment nodes of 0.4 under Scenario B-WT50P600R30E150 to 1.1 under Scenario B-WT150P200R30E0. The change in important flows for swimming, diving and grazing waterbirds under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-18). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the mean weighted change in flows across the catchment was negligible (0.4). It increased to 0.7 with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the negligible change across the assessment nodes from 1.1 to 0.6 (Figure 4-18). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for swimming, diving and grazing waterbirds ecology. P1064#yIS1 Figure 4-18 Habitat-weighted change in swimming, diving and grazing waterbirds flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for swimming, diving, and grazing waterbirds. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. Dams and changes in important flows for swimming, diving and grazing waterbirds Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.6) in important flow metrics when averaged across the 62 swimming, diving and grazing waterbirds assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for swimming, diving and grazing waterbirds was reduced to negligible (0.5). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a minor (3.2) weighted change in important flows averaged across the assessment nodes. This was reduced to negligible (0.8) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a minor mean change (3.5) occurred across the catchment without transparent flows. This was reduced to negligible (1.7) with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either of the single- dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for swimming, diving and grazing waterbirds (Figure 4-18). Under dam scenarios, habitat-weighted changes in important flows for swimming, diving and grazing waterbirds were greatest at node 9121015 (Figure 4-18) with a change in important flows at this single node recorded as extreme (51.5). Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR resulted in moderate (8.7) and major (19.5) percentile change, respectively, in important flows. These changes were reduced to moderate (5.1) and minor (3.7), respectively, when modelled with transparent flows. This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes. Climate change and water resource development for important flows for swimming, diving and grazing waterbirds Scenario CEdry resulted in a moderate change (5.1) in important flow metrics for swimming, diving and grazing waterbirds considering the mean across the 62 swimming, diving and grazing waterbirds assessment nodes (Figure 4-18). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.6) and B-WT150P600R30E0 (negligible; 0.6). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate change (both 5.4) when weighted across all swimming, diving and grazing waterbirds assessment nodes. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160P200R30 alone. Waterbird species in the swimming, grazing and diving waterbirds group are sensitive to changes in the depth, extent and duration of perennial semi-open and open deeper water environments such as wetlands (Section 4.4.10) and waterholes (Section 4.4.2) (Marchant and Higgins, 1990; McGinness, 2016). They can also be sensitive to changes in the type, density or extent of the fringing aquatic or semi-aquatic vegetation (Section 4.4.6) in and around these habitats. Such changes can occur when water is extracted directly from these habitats or when the time between connecting flows or rainfall events that fill these habitats is extended (Kingsford and Norman, 2002). Climate change, as explored through Scenario CEdry, and extremes are likely to interact with changes induced by water resource development, including inundation of freshwater habitats by seawater and inundation of nests by extreme flood events or seawater intrusion (Nye et al., 2007; Poiani, 2006; Traill et al., 2009a; Traill et al., 2009b). Reduced extent, depth and duration of inundation of waterhole and other deep-water environments is likely to reduce habitat availability and food availability for this group, increasing competition and predation and also increasing risk of disease and parasite spread. Conversely, species in this group that nest at water level or just above, such as the magpie goose, are particularly at risk of nests drowning when water depths increase unexpectedly (Douglas et al., 2005; Poiani, 2006; Traill et al., 2010; Traill et al., 2009a; Traill et al., 2009b). 4.3 Prawns, turtles and other species The members of this group are broad and distinct and include banana prawns, freshwater turtles and mud crabs. The members of this group include obligatory aquatic species as well as others that forage within the intertidal zone or can frequent the terrestrial habitats of riparian and floodplain habitats. Prawns and mud crabs inhibit marine and estuarine habitats, while freshwater turtles occupy rivers, lakes and wetlands within the freshwater portions of the catchment. Members of this group can have flow associations to support function and important life-history phases and connectivity between habitats and supply of nutrients. 4.3.1 Banana prawns Banana prawns are large decapods that are a prized fishery target species throughout their geographic distribution. Within the Northern Prawn Fishery, banana prawn catch supports a ‘sub- fishery’ that harvests approximately 4942 t (recent 10-year mean), which is mostly caught in the Gulf of Carpentaria and valued at about $70 to $80 million annually (Laird, 2021). Adult common banana prawns live and spawn offshore from the Southern Gulf catchments in waters 10 to 30 m deep; the larvae and postlarvae drift inshore to settle in the mangrove forest and mudbanks of estuarine mangrove habitats (Crocos and Kerr, 1983; Staples, 1980; Vance et al., 1998). In the Southern Gulf catchments, juvenile banana prawns inhabit the full extent of the estuary including saline tributaries. Adult banana prawn populations depend on emigration cues from freshwater river flows that reduce salinity and, at high-level freshwater flows, also reduce juvenile prawn food resources within estuarine habitats. These cues initiate banana prawn emigration to offshore habitats where a large population survives (Broadley et al., 2020; Duggan et al., 2014; Turschwell et al., 2022; Vance et al., 1998). Once offshore, their growth and survival is enhanced (Gwyther, 1982), possibly due in part to nutrient deposition in the flood plume (Burford et al., 2016). The key threats to banana prawns are associated with the loss of high-level flood flows that cue emigration from estuarine juvenile habitats to coastal near-shore adult habitats. Threats also arise with any reduction or temporal shift in low-level late-dry-season flows that that support facultative, brackish estuaries for juvenile banana prawn populations during October (approximately) to December, prior to wet-season floods. Asset flow dependencies analysis Banana prawns were assessed at the combined end-of-system node (9100000) in the Southern Gulf catchments (see Appendix A). Locations were selected for modelling banana prawns based upon habitat maps of the common banana prawn (Penaeus merguiensis) (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for banana prawns. When considering the change in important flow metrics, the hypothetical dam scenarios ranged from no measurable change (0) to a major change (17.6) under scenarios B-DGPC and B-D2, respectively. In contrast, water harvesting scenarios ranged from negligible (0.5) to moderate (6.6) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (14.3) in important flow metrics for banana prawns. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric low flood pulse count (<75th percentile). For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean winter discharge. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for banana prawns. Water harvesting and changes in important flows for banana prawns The hypothetical water harvesting scenarios resulted in a change in flows for banana prawns from negligible (0.5) to moderate (6.6) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. The change in important flows for banana prawns under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-19). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the change in flows across the catchment was negligible (1.2). This increased to minor (3) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river flow is below this threshold. With a target extraction volume of 150 GL, increasing the pump- start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change at the end-of-system from moderate (6.6) to minor (2.3) (Figure 4-19). Measures to protect important parts of the flow regime can support estuarine ecology. For example, results from other models suggest that reducing the extraction target volume of water extracted in any water year and increasing the pump-start threshold protects the low flows that are important for banana prawns’ ecology (Plagányi et al., 2024). Figure 4-19 Change in banana prawn flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for banana prawns. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1086#yIS1 Dams and changes in important flows for banana prawns Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in no measurable change (0) in important flow metrics for the banana prawns. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for banana prawns was not measurable (0). Scenario B-DGR (a dam on the perennial Gregory River) resulted in a larger change than Scenario B-DGPC, with a major change (17.6) in important flows at the end-of-system node. This was reduced to minor (4.5) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a major change (17.6) occurred without transparent flows. This was reduced to moderate (10.7) with provision of transparent flows. Scenario B-D2T (with transparent flows) resulted in a lower impact than scenarios without transparent flows, demonstrating the importance of providing flows to support estuarine habitats for banana prawns, particularly at the dry-season–wet-season interface when early-season flows create a brackish estuary more suitable as prawn habitat than hypersaline dry-season estuaries (Figure 4-19). Climate change and water resource development for important flows for banana prawns Scenario CEdry resulted in a moderate change (14.3) in important flow metrics for banana prawns. This indicates that the dry climate scenario led to a larger change than scenarios B-DGPC (negligible; 0) and B-WT150P600R30E0 (minor; 2.3). Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate (14.3) and major (15.9) change, respectively. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were similar to Scenario CEdry (Figure 4-19). Banana prawns’ life-history strategy renders them critically dependent on the natural flow regime in the Australian wet-dry tropics. Adult prawns spawn offshore, and postlarvae use currents to move shoreward to settle within estuarine benthic habitats before the annual wet season (Vance and Rothlisberg, 2020). In estuarine habitats, a brackish ecotone within the estuary supports lower mortality and faster growth (Staples and Heales, 1991; Vance et al., 1998; Wang and Haywood, 1999) before freshwater-cued emigration causes them to move to offshore marine habitats (Vance et al., 1998). Hence, both high-level pulsed flood flows and low-level early-wet-season flows are positive for the estuarine population of banana prawns. Water harvest had a moderate impact on banana prawns via flow modification. Such an affect could be the result of reducing wet-season flood flows and low flows. Estuaries can often be hypersaline during the annual recruitment window for juvenile prawns in the late dry season (Kenyon et al., 2004; Vance et al., 1990), and flows occurring during October to December can reduce environmental stress before the onset of the wet season. Water harvest may potentially affect both late-dry-season low flows and wet-season flood flows to the detriment of estuarine banana prawn growth and emigration. Dam construction had negligible to minor impacts on river flow. Predation on juvenile prawns by fish within the estuary can be high, and a significant proportion of the resident estuarine population is lost (Wang and Haywood, 1999). Wet-season flood flows cue juvenile banana prawns to emigrate to offshore habitats. The larger the flood the greater the emigration event (Staples and Vance, 1986), and emigrants probably benefit from nutrient deposition within the flood plume (Burford et al., 2012; Burford and Faggotter, 2021). Abundant adult populations of banana prawns, as measured by commercial catch in coastal marine habitats, are associated with higher flood flows from adjacent estuaries (Broadley et al., 2020; Duggan et al., 2019; Plagányi et al., 2023). Water harvesting reduces high flows in January, February and March during the wet season. Reduced high flows lowers the emigration cues within the estuary, so fewer prawns move to offshore waters where mortality in productive marine habitats is lower (Gwyther, 1982). Results from other modelling studies of mitigation measures for banana prawns within nearby northern Australian estuaries indicate that supporting seasonal critical flows via the provision of transparent flows past dams, or via higher pump-initiation thresholds before water extraction can occur, reduces the impacts on banana prawns (Plagányi et al., 2022; Plagányi et al., 2024). 4.3.2 Endeavour prawns Endeavour prawns are two species from the family Penaeidae, the blue (Metapenaeus endeavouri) and red (M. ensis) Endeavour prawn, both occurring in the marine region offshore from the Southern Gulf catchments (Laird, 2021). Commercially, they are grouped as a medium-sized decapod crustacean (40 to 60 g), and they are targeted by a commercial fishery in the Gulf of Carpentaria. Both species exhibit a larval life-history strategy (Dall et al., 1990) with inshore and offshore phases. Endeavour prawns are found in the south-western Gulf of Carpentaria in coastal waters about 10 to 45 m deep offshore from the rivers of the Southern Gulf catchments (Somers, 1994). The red Endeavour prawn is more common in the northern Gulf (Kenyon et al., 2021), while both species are found in the south-western Gulf near Mornington Island (Kenyon et al., 2021; Robertson et al., 1985; Somers, 1994). Seagrass habitats around Mornington Island are crucial nurseries for juveniles (Coles and Lee Long, 1985; Poiner et al., 1987). Within the coastal marine environment, Endeavour prawns are not directly influenced by river flows such as for freshwater emigration cues. However, their seagrass habitats are sensitive to changes in flow regime: high-volume turbid flood flows may reduce light levels in the littoral zone, limiting photosynthesis to the detriment of the seagrass community. Offsetting the impact of short-term turbid waters, flood flows deliver nutrients to coastal seagrass communities via flood plume deposition along the coast (Burford et al., 2012). Reduced high-level flows will modify flows compared to the historical flow regime and vary the provision of ecosystem services to coastal Gulf of Carpentaria habitats including seagrass beds. The key threats to Endeavour prawns are associated with modification of the historical flow regime and loss of ecosystem service provision to coastal seagrass nursery habitats. Asset flow dependencies analysis Endeavour prawns were assessed only at the combined end-of-system node (9100000) in the marine zone of the Southern Gulf catchments (see Appendix A). Locations were selected for modelling Endeavour prawns based upon habitat maps of blue Endeavour prawns (Metapenaeus endeavouri) and red Endeavour prawns (M. ensis), (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for Endeavour prawns. When considering the change in important flow metrics for Endeavour prawns, the hypothetical dam scenarios ranged from no measurable change (0) to a major change (26.3) under scenarios B-DGPCT and B-DGR, respectively. In contrast, water harvesting scenarios ranged from negligible (0.8) to moderate (9) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a major change (15.8) in important flow metrics for Endeavour prawns. Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 30-day means of daily discharge. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean December discharge. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for Endeavour prawns. Water harvesting and changes in important flows for Endeavour prawns The hypothetical water harvesting scenarios resulted in a change to the Endeavour prawn assessment node from negligible (0.8) to moderate (9) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. The change in important flows for Endeavour prawns under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-20). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the change in flows was negligible (1.9). This increased to minor (3.8) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change from moderate (9) to minor (2.8) (Figure 4-20). Measures to protect important parts of the flow regime can support catchment and coastal ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for Endeavour prawn ecology, such as for reducing coastal hypersalinity and providing nutrients to support food webs in coastal habitats. Figure 4-20 Change in Endeavour prawn flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for Endeavour prawns. Scenarios are ordered on the left axis by the magnitude of change with the change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1105#yIS1 Dams and changes in important flows for Endeavour prawns Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a no measurable change (0) in important flow metrics at the end-of-system assessment node. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for Endeavour prawns was not measurable (0). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a major change (26.3) in important flows. This was reduced to minor (3.6) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a major change (26.3) occurred without transparent flows. This was reduced to moderate (13.0) with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a flow change across the catchment similar to that of a single dam on the perennial Gregory River (Scenario B-DGR) due to the negligible impact of a dam on Gunpowder Creek (Scenario B-DGPC). Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows (13 compared to 26.3), demonstrating the importance of providing flows to support environmental outcomes for Endeavour prawns such as the provision of terrigenous nutrients in coastal habitats. Climate change and water resource development for important flows for Endeavour prawns Scenario CEdry resulted in a major change (15.8) in important flow metrics for Endeavour prawns (Figure 4-20). This indicates that the dry climate scenario led to a larger change than scenarios B-DGPC (no measurable change; 0) and B-WT150P600R30E0 (minor; 2.8). Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in major change (15.8 and 17.5, respectively). Coastal waters in Australia’s tropics, such as the Gulf of Carpentaria, support nutrient-limited, though productive littoral ecosystems (Burford et al., 2012; Burford and Faggotter, 2021) that become stressed by heat, high evaporation, hypersalinity and lack of precipitation for 9 months of the year (Blondeau-Patissier et al., 2014; Robins et al., 2020). The annual wet season delivers environmental flux that stimulates the ecosystem: marine biota benefit from the freshwater pulse flows increasing primary productivity (Blondeau-Patissier et al., 2014; Ndehedehe et al., 2020a). Though not yet well understood, littoral seagrass communities within the Gulf of Carpentaria and their dependent fauna could potentially benefit from the annual inputs to the system associated with wet-season flows (Plagányi et al., 2022). River regulation and climate change are key threatening processes for seagrass communities (Turschwell et al., 2021), which are the habitats of juveniles Endeavour prawns (Coles and Lee Long, 1985; Dall et al., 1990). Both prawn species shelter, forage and grow within vegetated habitat where leaf structure would reduce predation and promote primary productivity and prawn growth (Haywood et al., 1998; Kenyon et al., 1995). Floodwaters transport nutrients from the catchment to deposit within the flood plume and littoral zone adjacent to Gulf of Carpentaria rivers, supporting productivity in these habitats (Burford et al., 2012; Burford and Faggotter, 2021). 4.3.3 Tiger prawns Tiger prawns, including the grooved (Penaeus semisulcatus) and brown tiger prawns (P. esculentus), inhabit littoral and coastal ecosystems in northern Australia, particularly the Gulf of Carpentaria. These prawns exhibit distinct distributions influenced by sediment texture and latitude, with grooved tiger prawns found across the Indo-West Pacific and brown tiger prawns being endemic to Australia (Grey et al., 1983). In the Gulf of Carpentaria, they are especially abundant near Mornington Island in waters 10 to 45 m deep. Seagrass habitats are critical for juvenile tiger prawns, providing essential shelter and food, and are prevalent along the coastlines near Mornington Island (Coles and Lee Long, 1985; Poiner et al., 1987). In the Southern Gulf catchments, tiger prawns are a significant fishery resource with most catches concentrated in the western Gulf, particularly near Mornington Island (Laird, 2021). Adult tiger prawns live and spawn offshore, and juveniles settle in inshore seagrass beds, which are vital for their development. The species distribution in the Southern Gulf catchments is dominated by brown tiger prawns, especially in the Mornington and Sweers reporting regions (Kenyon et al., 2021; Robertson et al., 1985; Somers, 1994). Despite the importance of these habitats, juvenile populations along the broader Gulf of Carpentaria coastline remain under-sampled, though their presence has been confirmed in specific areas (Coles and Lee Long, 1985). The key threats to tiger prawns are seagrass habitat loss due to disturbance such as cyclones (Lonegan et al., 2013). Asset flow dependencies analysis Tiger prawns were assessed only at the combined end-of-system node (9100000) in the marine zone of the Southern Gulf catchments (see Appendix A). Locations were selected for modelling tiger prawns based upon habitat maps of the grooved tiger prawn (Penaeus semisulcatus) and the brown tiger prawns (P. esculentus) (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for tiger prawns. When considering the change in important flow metrics at the end-of-system node, the hypothetical dam scenarios ranged from no measurable change (0) to a major change (23.9) under scenarios B-DGPCT and B-D2T, respectively. In contrast, water harvesting scenarios ranged from negligible (0.6) to moderate (8.7) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (12.6) in important flow metrics for tiger prawns. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 30-day means of daily discharge. For Scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean December discharge. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for tiger prawns. Water harvesting and changes in important flows for tiger prawns The hypothetical water harvesting scenarios resulted in a change at the end-of-system node for tiger prawns ranging from negligible (0.6) to moderate (8.7) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. The change in important flows for tiger prawns under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-21). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the change in flows was negligible (1.5). This increased to minor (3.0) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river flow level is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes from moderate (8.7) to minor (2.3) (Figure 4-21). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for tiger prawns’ ecology such as for reducing coastal hypersalinity and providing nutrients to support food webs in coastal habitats. Figure 4-21 Change in tiger prawns flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for tiger prawns. Scenarios are ordered on the left axis by the magnitude of change with the change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1123#yIS1 Dams and changes in important flows for tiger prawns Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in no measurable change (0) in important flow metrics at the combined end-of-system node (9100000). When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for tiger prawns remained not measurable (0). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a major change (22.7) in important flows at the assessment node. This was reduced to minor (4.8) with the provision of transparent flows under Scenario B-D GRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a major change (22.7) occurred without transparent flows. This was increased to major (23.9) with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a larger flow change than either of the single-dam scenarios as reduced flow levels lessen freshwater incursion within coastal habitats during the wet season (Figure 4-21). Climate change and water resource development for important flows for tiger prawns Scenario CEdry resulted in a moderate change (12.6) in important flow metrics for tiger prawns (Figure 4-21). This indicates that the dry climate scenario led to a larger change than scenarios B-DGPC (no measurable change; 0) and B-WT150P600R30E0 (minor; 2.3). Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in moderate change (12.6 and 14, respectively). This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were similar to or higher than Scenario CEdry, but lower than two dams within the catchment (Scenario B-D2) and higher than Scenario B-WT150P200R30 alone. 4.3.4 Freshwater turtles In northern Australia, freshwater turtles occupy a range of aquatic habitats, including both river and floodplain wetland habitats such as main channels, waterholes and oxbow lakes (Cann and Sadlier, 2017; Thomson, 2000). Turtles inhabit the freshwater reaches of the Southern Gulf catchments and depend upon the seasonal wet-season flows to support habitat and movement needs. Many of the freshwater turtle species in northern Australia have developed adaptive traits to survive in the inter-annual variation between the wet and dry seasons, such as the emergence of hatchlings with the wet-season onset (Cann and Sadlier, 2017). During the dry season, the movements of the freshwater turtles on and off the floodplain are limited, making them more vulnerable to changes in water quality, invasive species and habitat degradation (Cann and Sadlier, 2017; Doupe et al., 2009). Therefore, changes to hydrology (particularly riverine–wetland connectivity), habitat loss and climate change are some of the key threatening processes for freshwater turtles (Stanford et al., 2020). Four of the ten freshwater turtle species found in the NT have been recorded in the Southern Gulf catchments: Cann’s snake-necked turtle (Chelodina canni), northern snake-necked turtle (C. oblonga), Gulf snapping turtle (Elseya lavarackorum) and red-bellied short-necked turtle (Emydura subglobosa). Records for the Southern Gulf catchments are sparse compared to many other regions of Australia. Currently all four species are listed as Least concern by the NT Government (Department of Environment Parks and Water Security, 2019); however, the Gulf snapping turtle is listed nationally as Endangered under the EPBC Act. The analysis considers change in flow regime and related habitat changes but does not consider the addition or loss of potential habitat associated with the creation of a dam impoundment or instream structures (see also Yang et al. (2024) for dam impoundments). Asset flow dependencies analysis Freshwater turtles were assessed across a total of 3948 km of river reaches in the Southern Gulf catchments with contributing flows from 62 model nodes (see Appendix A). Some of the key river reaches for freshwater turtles within the Assessment catchments were modelled downstream of nodes 9121161, 9129042 and 9121015. Locations were selected for modelling freshwater turtles based upon species distribution models of the northern snake-necked turtle (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for freshwater turtles. When considering the mean weighted change in important flow metrics across all 62 freshwater turtles analysis nodes, the hypothetical dam scenarios ranged from negligible (0.6) to minor (3.7) for change in flow under scenarios B-DGPCT and B-D2, respectively. In contrast, water harvesting scenarios remained negligible (0.5 and 1.4) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (5.2) in important flow metrics for freshwater turtles. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for freshwater turtles (Figure 4-22). Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 30-day means of daily discharge at node 9121015. For Scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean November discharge at node 9121015. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow- ecology relationships for freshwater turtles. Figure 4-22 Spatial heatmap of habitat-weighted changes in flow for freshwater turtles, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for freshwater turtles weighted by the habitat value of each reach. P1136#yIS1 Water harvesting and changes in important flows for freshwater turtles The hypothetical water harvesting scenarios resulted in a negligible mean change of 0.5 under Scenario B-WT50P600R30E250 and 1.4 under Scenario B-WT150P200R30E0 across freshwater turtles assessment nodes. The change in important flows for freshwater turtles under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-23). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the mean weighted change in flows across the catchment was negligible (0.6). It increased to 0.9 with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes from 1.4 to 0.8 (both negligible) (Figure 4-23). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for freshwater turtle ecology. Figure 4-23 Habitat-weighted change in freshwater turtles flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for freshwater turtles. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1141#yIS1 Dams and changes in important flows for freshwater turtles Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.7) in important flow metrics when averaged across the 62 freshwater turtle assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for freshwater turtles was reduced to 0.6. Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a minor weighted change (3.4) in important flows averaged across the assessment nodes. This was reduced to negligible (1.3) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a minor mean change (3.7) occurred across the catchment without transparent flows. This was reduced to minor (2.1) with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either of the single-dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for freshwater turtles (Figure 4-23). Under dam scenarios, habitat-weighted changes in important flows for freshwater turtles were greatest at node 9121015 (Figure 4-23), with a change in important flows at this single node recorded as extreme (43.3). Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR resulted in moderate (12.4) and extreme (36) percentile change, respectively, in important flows. These changes were reduced to moderate (8.9 and 9.5, respectively) when modelled with transparent flows. This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes. Dams in the Southern Gulf catchments can significantly alter hydrological patterns through water extraction and flow barriers. These changes may affect the distribution, growth and reproduction of freshwater species, including turtles, making them more vulnerable (Hunt et al., 2013). The resulting loss of connectivity (see Yang et al. (2024)) through fragmentation and habitat loss can disrupt turtle nesting sites and refugia and limit their movement among wetlands (Bodie and Semlitsch, 2000; Bowne et al., 2006). Climate change and water resource development for important flows for freshwater turtles Scenario CEdry resulted in a moderate change (5.2) in important flow metrics for freshwater turtles considering the mean across the 62 freshwater turtle assessment nodes (Figure 4-23). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.7) and B-WT150P600R30E0 (negligible; 0.8). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in moderate change (5.9 and 5.8, respectively) when weighted across all freshwater turtle assessment nodes. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160P200R30 alone. Scenario D-DGPC with both water harvesting and dry climate change would further risk reducing dry-season baseflows across a larger area in the catchment. Therefore, the available suitable habitat supported by flows decreases under this scenario with potentially longer or more severe periods of dry conditions. Such a baseflow reduction could even shift the rivers from perennial to intermittent status, which can lessen the turtles’ chances of reaching a freshwater shelter for the dry season (Hunt et al., 2013). Changes to the inundation and flow regimes reduce freshwater turtles’ feeding and the suitability of habitats such as waterholes (Warfe et al., 2011), which increases the competition for resources (Chessman, 1988; Queensland Department of Science, Information Technology, Innovation and the Arts, 2014). The development of dams in the Southern Gulf catchments as part of the water resource development in northern Australia has the potential to change the catchment’s hydrological pattern through water extraction and the creation of barriers to flow. These changes can affect the distribution, population growth and reproduction of freshwater species (Hunt et al., 2013) and make freshwater turtles more vulnerable. The loss of connectivity (fragmentation and habitat loss – see Yang et al. (2024)) resulting from new infrastructure, can disrupt turtle nesting sites and refugia, and it can also restrict their emigration and dispersal among wetlands (Bodie and Semlitsch, 2000; Bowne et al., 2006). 4.3.5 Mud crabs In the Southern Gulf catchments, mud crabs (Scylla serrata and small numbers of S. olivacea) occupy the estuary of the river and shallow coastal habitats north and south of the river mouth. Mud crabs are an ecologically important crustacean capable of modifying the estuarine habitats throughout Australia’s wet-dry tropics (Pati et al., 2023; Robins et al., 2020). Within mangrove forests, adult mud crabs re-work mud substrates and play a significant trophic role in mangrove ecosystems. Mud crabs consume 650 kg biomass per hectare per year in the mangrove forest and 2100 kg biomass per hectare per year in mangrove fringe habitat (Alberts-Hubatsch et al., 2016). Mud crabs are targeted by commercial, recreational and Indigenous fisheries. Mud crabs are important species for Indigenous Peoples in northern Australia, both culturally (Finn and Jackson, 2011; Jackson et al., 2011) and as a historical and current food source (Naughton et al., 1986). The presence of S. olivacea in regions of the Gulf of Carpentaria in poorly understood. They have been identified from the Weipa region, north-east Gulf of Carpentaria, but it is suggested that they are not found elsewhere in the Gulf of Carpentaria. Regardless, their percentage composition of the commercial or recreational mud crab catch is negligible throughout the Gulf of Carpentaria. Therefore, references to mud crabs in this report will mean S. serrata, the dominant species in Australian coastal ecosystems. Analyses of environmental drivers and mud crab catches in the Gulf of Carpentaria show that river flows enhance catch, but high air temperature over the wet season has a dominant negative influence on mud crab abundance within the western Gulf of Carpentaria estuarine habitats (Robins et al., 2020). Brackish estuaries provide optimal conditions for the growth and survival of juvenile mud crabs. Hence, the loss of low-level flows and flood flows would affect the mud crab population in the Southern Gulf rivers. Asset flow dependencies analysis Mud crabs were assessed at the combined end-of-system node (9100000) in the Southern Gulf catchments (see Appendix A). Locations were selected for modelling mud crabs based upon habitat maps of the mud crab (Scylla serrata) (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for mud crabs. When considering the weighted change in important flow metrics at the end-of-system node, the hypothetical dam scenarios ranged from no measurable change (0) to major change (21.1) under scenarios B-DGPCT and B-DGR, respectively. In contrast, water harvesting scenarios ranged from negligible (0.4) to moderate (10.3) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (14.8) in important flow metrics for mud crabs. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 90-day means of daily discharge. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean November discharge. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for mud crabs. Water harvesting and changes in important flows for mud crabs The hypothetical water harvesting scenarios resulted in a flow change for mud crabs at the end-of- system node from negligible (0.4) to moderate (10.3) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. The change in important flows for mud crabs under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-24). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the change in flows was negligible (0.9). This increased to minor (2.4) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump- start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change at the assessment node from moderate (10.3) to negligible (1.9) (Figure 4-24). Measures to protect important parts of the flow regime can support estuarine ecology. For example, reducing the extraction target volume of water extracted in any water year, maintaining historical annual flow patterns to the benefit of mud crabs (Blamey et al., 2023), and increasing the pump-start threshold protects the low flows and early-season flows that are important for mud crab populations (Blamey et al., 2023). Figure 4-24 Change in mud crabs flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for mud crabs. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1160#yIS1 Dams and changes in important flows for mud crabs Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in no measurable change (0) at the end-of-system node. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for mud crabs remained not measurable (0). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a major change (21.1) in important flows. This was reduced to minor (4.6) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a major change (21.1) occurred across the catchment without transparent flows. This was reduced to moderate (13.6) with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either of the single-dam scenarios. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for mud crabs. Rainfall and high levels of river flow have been shown to be positively related to mud crab catch, and seasonal freshwater inflows to downstream estuarine habitats support brackish ecotones which enhance the habitat of juvenile crabs during annual recruitment following offshore spawning (Robins et al., 2020). Climate change and water resource development for important flows for mud crabs Scenario CEdry resulted in a moderate change (14.8) in important flow metrics for mud crabs considering change at the end-of-system node (Figure 4-24). This indicates that the dry climate scenario led to a larger change than scenarios B-DGPC (negligible; 0) and B-WT150P600R30E0 (negligible; 1.9). Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate (14.8) and major (15.9) change, respectively. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were similar to Scenario CEdry, lower than the impact of two dams within the catchment (scenario B-D2) but higher than the water harvest scenario (B-WT160P200R30) alone. In the western Gulf of Carpentaria, the short-duration wet season (3 months at most) and unreliability of annual rainfall (including consecutive years of low rainfall) render mud crab populations highly vulnerable to climate events, especially cumulative heat from November to March (Robins et al., 2020). The life history of mud crabs would be significantly affected by any major interruptions to the natural flows of northern Australian rivers. Juvenile and adult mud crabs can tolerate a wide range of salinities. They live in estuarine and littoral-coast habitats which are both supported by freshwater inflows (Alberts-Hubatsch et al., 2016). In the Southern Gulf catchments, scenario analysis suggests that placing a single dam on the perennial Gregory River or developing multiple-dam infrastructure (e.g. B-DGR and B-D2) will have a major impact on mud crabs via flow reduction. However, the effects of flow can be reduced to minor and moderate when dams are constructed to allow flows to pass the dam wall and mimic historical flow regimes, especially when river flow levels are low. Water harvesting at all extraction levels (scenarios B-W T50P600R30E0 to B-W T300P600R30E0) has negligible to minor change to mud crab flow dependencies by reducing key aspects of annual flow. Juvenile mud crabs benefit from perennial baseflows and low-to-medium flood flows that create brackish conditions in an estuary (Alberts-Hubatsch et al., 2016; Welch et al., 2014). The optimal conditions for mud crabs are temperatures of 25 to 30 °C and salinity of 10 to 30 ppt (Ruscoe et al., 2004). Estuaries in the Australian tropics often are hypersaline before the wet season; hence, growth and survival of mud crabs would be inhibited if water regulation or extraction significantly reduced first-season low flows. While mud crabs are not triggered by an emigration cue, analysis of environmental factors and commercial catch by Robins et al. (2020) showed that higher river flow and lower water stress (caused by rainfall and/or evaporation: less stress if rainfall is high) had positive effects on mud crab catch in the Southern Gulf catchments marine region. Elsewhere in the Gulf of Carpentaria, river flow and rainfall also have been shown to be positively related to mud crab catch (Robins et al., 2020). High-level wet-season flows and October to December low-level flows that occur as a result of early-season precipitation reduce environmental stress that persists in estuarine habitats during the extended months with negligible rainfall (approximately April to December). The loss of either of these characteristic flows due to water resource development, especially water harvesting, would reduce flows in the Southern Gulf rivers and have downstream negative impacts on estuarine mud crabs (Blamey et al., 2023). Mean sea-level anomaly during the wet season and the Southern Oscillation Index were positive for catches in this region (Plagányi et al., 2022; Robins et al., 2020). 4.4 Freshwater-dependent habitats The members of this group include floodplain wetlands, inchannel waterholes, mangroves, saltpans and salt flats, seagrass and surface-water-dependent vegetation communities. These habitat groups span freshwater and marine waters or a combination of both. Members of this group can have flow associations to support ecological function and support a diverse range of species during different flow conditions or times of the year. 4.4.1 Floodplain wetlands For the purpose of this analysis, floodplain wetlands are defined as freshwater lakes, ponds, swamps and floodplains with water that can be permanent, seasonal or intermittent, and can be natural or artificial. The Southern Gulf catchments contain 13 nationally significant wetlands listed in the Directory of Important Wetlands Australia (DIWA), although none of them are listed under the Ramsar Convention (see Merrin et al. (2024)). Wetlands provide permanent, temporary or refugia habitat for a range of species, are important for driving both primary and secondary productivity, and provide a range of additional ecosystem services (Junk et al., 1989; Mitsch et al., 2015; Nielsen et al., 2015; van Dam et al., 2008a; Ward and Stanford, 1995). Floodplain wetlands are highly influenced by the timing, duration, extent and magnitude of floodplain inundation, which can have a significant impact on the ecological values, including species diversity, productivity and habitat structure (Close et al., 2015; Tockner et al., 2010). In the southern Gulf of Carpentaria, during high-level flood flows, floodplain wetlands connect with coastal salt flats as a ‘shallow lake’ continuum. Freshwater fauna move downstream and brackish-water-tolerant estuarine species move from the river channels onto the inundated, productive salt flats to forage and reproduce. There they take advantage of the food web that is dependent on the floodwater-stimulated algal crusts that revive from their dry-season senescence (Burford et al., 2016; Burford et al., 2010). The key threats to floodplain wetlands are associated with changes in flood regimes, including the timing, duration, extent and magnitude of floodplain inundation. Asset flow dependencies analysis Floodplain wetlands were assessed across a total of 2027 km of river reaches in the Southern Gulf catchments with contributing flows from 34 model nodes (see Appendix A). Some of the key river reaches for floodplain wetlands within the Assessment catchments were modelled downstream of nodes 9139000, 9130140 and 9130050. Locations were selected for modelling floodplain wetlands based upon wetland and floodplain mapping (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for floodplain wetlands. When considering the mean weighted change in important flow metrics across all 34 floodplain wetlands analysis nodes, the hypothetical dam scenarios ranged from negligible (0.3) to minor (3.6) for change in flow under scenarios B-DGPCT and B-D2T, respectively. In contrast, water harvesting scenarios ranged from negligible (0.6) to minor (2.3) under scenarios B-WT50P600R30E0 and B-WT300P600R30E0, respectively. Scenario CEdry resulted in a moderate change (12.9) in important flow metrics for floodplain wetlands. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for floodplain wetlands (Figure 4-25). It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric high flood pulse count 1 (10th percentile) at node 91210150. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean November discharge at node 9121012. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for floodplain wetlands. Figure 4-25 Spatial heatmap of habitat-weighted changes in flow for floodplain wetlands, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for floodplain wetlands weighted by the habitat value of each reach. P1179#yIS1 Water harvesting and changes in important flows for floodplain wetlands The hypothetical water harvesting scenarios resulted in a mean change across floodplain wetland assessment nodes ranging from negligible (0.6) to minor (2.3) under scenarios B-WT50P600R30E0 and B-WT300P600R30E0, respectively. The change in important flows for floodplain wetlands under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-26). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the mean weighted change in flows across the catchment was negligible (0.6). It increased to minor (2.3) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the negligible change across the assessment nodes from 1.6 to 1.5 (Figure 4-26). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows. Figure 4-26 Habitat-weighted change in floodplain wetlands flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for floodplain wetlands. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1184#yIS1 Dams and changes in important flows for floodplain wetlands Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in a negligible percentile change (0.3) in important flow metrics when averaged across the 34 floodplain wetlands assessment nodes. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for floodplain wetlands remained negligible (0.3). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a minor (3.2) weighted change in important flows averaged across the assessment nodes. This remained minor with only a slight increase (3.3) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a minor mean change (3.4) occurred across the catchment without transparent flows. This remained minor but with a slight increase (3.6) with the provision of transparent flows. Scenario B-D2 resulted in a larger mean flow change across the catchment than either of the single-dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Under dam scenarios, habitat-weighted changes in important flows for floodplain wetlands were greatest at node 9121050 (Figure 4-26), with a change in important flows at this single node recorded as major (26.5). The nodes directly downstream of the dam in Scenario B-DGR resulted in major percentile change (26.5) in important flows. Changes were reduced but still major (22.5) when modelled with transparent flows (Scenario B-DGRT). This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes. Dams can have a significant impact on floodplain wetlands, as they capture runoff from rainfall events that would otherwise spill onto floodplains during larger events, facilitating the connection of the wetlands to the main river channel. The reduction in flood magnitude due to dams can change the connectivity between the river channel and the floodplain wetlands, significantly affecting the size of the inundated area. A loss of connectivity between the river channel and the floodplain wetland may also occur. This disconnection can alter the frequency and duration of wetland inundation, potentially leading to changes in the structure, function and biodiversity of these wetland habitats (Poff and Zimmerman, 2010; Richter et al., 1996). Climate change and water resource development for important flows for floodplain wetlands Scenario CEdry resulted in a moderate change (12.9) in important flow metrics for floodplain wetlands considering the mean across the 34 floodplain wetland assessment nodes (Figure 4-26). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.3) and B-WT150P600R30E0 (negligible; 1.5). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in moderate change (13.2 and 13.9, respectively), when weighted across all floodplain wetland assessment nodes. This shows that the changes of scenarios D-DGPC or D-WT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160P200R30 alone – a large portion of this change in flow regimes was associated with the drying climate. Lateral connectivity analysis The lateral connectivity within the Southern Gulf catchments was modelled using floodplain hydraulics (e.g. depth, velocity) and inundation dynamics for a 2016 modelled flood event with a 33.3% AEP, and a 2023 modelled flood event with a 2.6% AEP. The modelling included development scenarios (dam scenarios (B-D) and water harvesting scenarios (B-W)), as well as future climate (CEdry and CEwet) and combinations of future climate and development scenarios (Ddry-D and Ddry-W) (see Section 2.2.2, and Karim et al. (2024)). For the 2016 modelled flood event, there was a major difference between Scenario B-D and Scenario B-W compared to Scenario AE (21.7% and 3.9% reduction, respectively) (Table 4-1 and Figure 4-27). For the 2023 modelled flood AEP event, Scenario B-W had negligible impact on floodplain inundation compared to Scenario AE (0.4% reduction across the full model domain), with only two sites showing a decrease (Table 4-1). Scenario B-D however, had a minor reduction in the area inundated compared to Scenario AE (4.6% reduction across the full model domain; Table 4-1 and Figure 4-28). The reduction in area inundated under Scenario B-W was proportionally larger under the smaller 2016 modelled flood event compared to the 2023 modelled flood event (3.9% and 0.4% respectively) as the same extraction target was used for both events. Therefore, water harvesting will have a greater impact on smaller flood events, assuming pump thresholds are met. Water harvesting reduces the flow within a river channel, reducing inundation onto the floodplain (Kingsford, 2000). There was a major reduction in area inundated under Scenario B-D between the 2016 modelled flood event compared to the 2023 modelled flood event (21.5% and 4.6% reduction compared to Scenario AE respectively; Table 4-1 and Table 4-2). Dams capture moderate to large flows, preventing flood pulses and reducing inundation onto the floodplain (Kingsford, 2000). However, very large flood events will still inundate the floodplain. Scenario CEdry had a greater reduction as a percentage of the total inundated area in the smaller flood event, compared to the larger flood event (23.3% and 17.6% reduction compared to Scenario AE respectively; Table 4-1 and Table 4-2). Whereas Scenario CEwet had a greater increase as a percentage of the total inundated area for the larger flood event, compared to the smaller flood event compared to Scenario AE, although the differences were negligible (14.7% and 13.8% increase respectively; Table 4-1 and Table 4-2, Figure 4-27 and Figure 4-28). Table 4-1 Maximum floodplain inundation (in km2) and percentage change from Scenario AE as the maximum flood extent for each scenario for 2016 modelled flood event in the Southern Gulf catchments Scenario AE is in km2, and as percentage change from Scenario AE for each of the scenarios shown as a change in the maximum flood extent for each scenario for a 2016 modelled flood event. Site locations are shown in Figure 2-5. SITE AE B-D B-W CEDRY CEWET DDRY-D DDRY-W 1 0 0 0 0 0 0 0 2 12.5 0.5 -1.3 -13.0 5.0 -11.8 -11.9 3 56.0 -1.1 0.1 -14.2 4.8 -16.9 -15.1 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 28.2 1.8 -3.7 -12.6 4.9 -12.1 -29.0 7 70.2 -23.5 -2.5 -16.3 7.2 -52.5 -12.0 8 34.1 -24.0 -0.1 -9.9 5.7 -25.4 -1.5 9 480.1 -32.7 -2.8 -28.2 11.5 -66.7 -14.3 Model domain 1534.3 -21.5 -3.9 -23.3 13.8 -44.2 -19.5 Table 4-2 Maximum floodplain inundation (in km2) and percentage change from Scenario AE as the maximum flood extent for each scenario for a 2023 modelled flood event in the Southern Gulf catchments Scenario AE is in km2, and as percentage change from Scenario AE for each of the scenarios shown as change in the maximum flood extent for each scenario for a 2023 modelled flood event. Site locations are shown in Figure 2-5. SITE AE B-D B-W CEDRY CEWET DDRY-D DDRY-W 1 8.5 0.0 0.0 -0.6 2.2 -1.6 -10.8 2 71.6 0.7 -0.1 -8.4 7.3 -8.3 -22.7 3 66.2 0.0 0.0 0.0 0.0 0.0 0.0 4 6.2 0.0 0.0 -0.9 0.0 -2.2 -2.1 5 25.1 -1.2 0.0 -1.2 -1.2 -10.9 0.0 6 59.9 0.7 -0.3 -0.7 1.1 -0.7 -10.3 7 147.7 -0.7 0.0 -2.6 1.1 -8.1 -2.1 8 49.0 -1.4 0.0 -1.9 0.9 -4.1 -0.1 9 925.4 -0.3 0.0 -2.8 0.6 -4.9 -3.3 Model domain 6775.0 -4.6 -0.4 -17.6 14.7 -29.7 -17.6 Figure 4-27 Time series of the floodplain inundation for each scenario for the 2016 modelled flood event in the Southern Gulf catchments Figure 4-28 Time series of the floodplain inundation for each scenario for the 2023 modelled flood event in the Southern Gulf catchments The greatest impact on the area of floodplain wetland inundation were the scenarios for future climate and future development (scenarios Ddry-D and Ddry-W). The impact on the area inundated was proportionally larger for the smaller 2016 modelled flood event compared to the 2023 modelled flood event (44.2% and 29.7% reduction for Ddry-D compared to Scenario AE and 19.5% and 17.6% reduction for Ddry-W compared to Scenario AE; Table 4-1 and Table 4-2). The spatial distribution of floodplain inundation under the different scenarios shows that the smaller wetlands were less likely to inundate under a future drying climate and future development scenarios, with flows more likely to be restricted to the channel (Figure 4-29 and Figure 4-30). P1402#yIS1 P1404#yIS1 For the 2016 modelled flood event, 3 sites did not connect at all to the river channel (sites 1, 4 and 5), including under Scenario AE (Table 4-1). Site 1 is the DIWA listed site, Bluebush Swamp, with site 4 the neighbouring wetland. Both sites are off the main river channel and are palustrine wetlands, providing important habitat for waterbirds (Department of Agriculture‚ Water and the Environment, 2021a). However, there was some connection to the channel with the 2023 modelled flood event, with most scenarios having a negligible to minor impact (Table 4-2). Site 7 includes land subject to inundation that forms part of the Nicholson Delta Aggregation DIWA listed site. This site is located downstream of several proposed water harvesting sites and the proposed Gregory River and Nicholson River dams. Water harvesting only had a minor impact on the area inundated (2.5% reduction for the 2016 modelled flood event, and 0% change in the 2023 modelled flood event; Table 4-1 and Table 4-2), due to the requirement of meeting pump thresholds for the smaller events. The proposed dam scenarios had a larger impact on the area inundated for the 2016 modelled flood event (23.5% reduction; Table 4-1), but a negligible impact on the larger 2023 modelled flood event (0.7% reduction; Table 4-2). The biggest impact on the area inundated for this site was for Scenario Ddry-D (52.5%; Table 4-1). This DIWA site provides refugia habitat for waterbirds in the dry season (Department of Agriculture‚ Water and the Environment, 2021a). However, under Scenario Ddry-D, this habitat would be significantly reduced, likely leading to a decrease in the bird populations it can support. Site 9 includes salt flats that form part of the Southern Gulf Aggregation DIWA listed site. This site was significantly impacted by the development of dams and a drying climate for the smaller flood events when compared to Scenario AE (Scenario B-D had a reduction of 32.7%, Scenario CEdry had a reduction of 28.2%, and Scenario Ddry-D had a reduction of 66.7%; Table 4-1). The larger flood event had a negligible to minor impact on the area inundated compared to Scenario AE (Table 4-2). Freshwater flooding during the wet season will affect the overall productivity of the system by reducing the exchange of nutrients and carbon between the floodplain wetlands and the river channel (Brodie and Mitchell, 2005; Hamilton, 2010). This exchange of carbon and nutrients will be important for maintaining the health of the mangrove communities at this site (Abrantes et al., 2015). This will also affect the available habitat for birds and mud crabs. Mud crabs occupy mangrove forests and nearby shallow subtidal habitats within estuarine and coastal ecosystems (Alberts-Hubatsch et al., 2016). The area is also considered one of the most important shorebirds sites in Australia (Department of Agriculture‚ Water and the Environment, 2021a). Overall, the loss of floodplain wetland connectivity to the river channel will reduce the available habitat for species such as fish and birds, as well as mud crabs. Mangroves and floodplain vegetation species that require inundation may also be affected. As a result, there is a risk of these areas transitioning into more terrestrial environments (Kingsford, 2000; Pettit et al., 2017). Figure 4-29 Maximum floodplain inundation for each scenario for the 2016 modelled flood event in the Southern Gulf catchments Scenarios are: (a) AE, (b) B-D, (c) B-W, (d) CEdry, (e) CEwet, (f) Ddry-D and (g) Ddry-W. Note: The maximum extent may occur at a different time step between scenarios. P1412#yIS1 Figure 4-30 Maximum floodplain inundation for each scenario for the 2023 flood modelled event in the Southern Gulf catchments Scenarios are: (a) AE, (b) B-D, (c) B-W, (d) CEdry, (e) CEwet, (f) Ddry-D and (g) Ddry-W. Note: The maximum extent may occur at a different time step between scenarios. P1416#yIS1 4.4.2 Inchannel waterholes For the purpose of this analysis, inchannel waterholes are defined as locations within the river channel in which water persists during periods of dry conditions (for comparison, see Section 4.4.1 for floodplain wetlands). Waterholes are found broadly across the Southern Gulf catchments, including sections of river around Lawn Hill, Gregory and Mount Isa (see Merrin et al. (2024)). The highly anabranching channels lower in the catchment (towards the Gulf of Carpentaria) limit the size of waterholes that persist and that can be identified by remote sensing to all but the larger channels. In many locations, persistent waterholes are supported by groundwater discharge that maintains an often-significant level of baseflow during periods that would otherwise result in highly reduced flow or cease-to-flow conditions. Permanent or near-permanent flowing river sections and springs that originate from the limestone plateau feeding into Lawn Hill Creek include Settlement Creek (Bureau of Meteorology, 2017) and west of Gregory in Boodjamulla National Park. In other areas, however, many tributaries demonstrate the seasonal ephemeral flows that are broadly characteristic of northern Australian rivers (Petheram et al., 2008). In ephemeral river systems, the waterholes that retain water for periods sufficient to outlast dry spells provide vital refuge habitat and resources for both flora and fauna (Sheldon, 2017). In the Southern Gulf catchments, these biodiversity values are highlighted by the waterholes providing habitat for species listed under the EPBC Act, including the freshwater sawfish, which is listed as Vulnerable (Section 4.1.5). Waterholes are sensitive to changes in low-flow magnitudes, low-flow duration, periods of cease- to-flow and timing of first-wet-season inflows. Asset flow dependencies analysis Inchannel waterholes were assessed across a total of 3560 km of river reaches in the Southern Gulf catchments with contributing flows from 57 model nodes (see Appendix A). Some of the key river reaches for inchannel waterholes within the Assessment catchments were modelled downstream of nodes 9139000, 9130150 and 9130140. Locations were selected for modelling inchannel waterholes based upon analysis of remote sensing data (see Merrin et al. (2024) and Sims et al. (2016)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for inchannel waterholes. When considering the mean weighted change in important flow metrics across all 57 inchannel waterhole analysis nodes, the hypothetical dam scenarios ranged from negligible (0.4) to moderate (8.3) for change in flow under scenarios B-DGPCT and B-D2, respectively. In contrast, water harvesting scenarios ranged from negligible (0.3) to minor (2.1) under scenarios B-WT50P600R30E150 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (8.4) in important flow metrics for inchannel waterholes. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for inchannel waterholes (Figure 4-31). Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual minima of 30-day means of daily discharge at node 9121011. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric low flow pulse duration (75th percentile) at node 9121015. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for inchannel waterholes. Figure 4-31 Spatial heatmap of habitat-weighted changes in flow for inchannel waterholes, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for inchannel waterholes weighted by the habitat value of each reach. P1425#yIS1 Water harvesting and changes in important flows for inchannel waterholes The hypothetical water harvesting scenarios resulted in a mean change across inchannel waterholes assessment nodes ranging from negligible (0.3) to minor (2.1) under scenarios B-WT50P600R30E150 and B-WT150P200R30E0, respectively. The change in important flows for inchannel waterholes under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-32). Under Scenario B-WT50P600R30E0, with a low extraction target volume of 50 GL, the mean weighted change in flows across the catchment was negligible (0.3). It remained negligible (0.3) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes from minor (2.1) to negligible (0.3) (Figure 4-32). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for inchannel waterhole ecology. In the context of water resource development in the Southern Gulf catchments, the development of water resources, including dam construction and water harvesting, has the potential to reduce flows and influence the natural filling and drying cycles of waterholes (Arthington et al., 2010; McJannet et al., 2014; Waltham et al., 2013). Waterholes persist because of the hydrological balance within the system, affected by the timing and duration of both filling events and drawdown (Close et al., 2012). Figure 4-32 Habitat-weighted change in inchannel waterhole flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for inchannel waterholes. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1430#yIS1 Dams and changes in important flows for inchannel waterholes Under the dam scenarios, Scenario B-DGPC resulted in a negligible percentile change (0.4) in important flow metrics when averaged across the 57 inchannel waterholes assessment nodes (Figure 4-32). When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for inchannel waterholes remained negligible (0.4). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a moderate (8.2) weighted change in important flows averaged across the assessment nodes. This was reduced to negligible (0.6) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a moderate (8.3) mean change occurred across the catchment without transparent flows. This was reduced to minor (3.8) with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a larger mean flow change across the catchment than either of the single-dam scenarios. This increase was due to the combined effects on flows downstream of the two dams and the larger portion of the catchment affected. Scenario B-D2T (with transparent flows) resulted in a smaller change than scenarios without transparent flows, demonstrating the importance of providing flows to support environmental outcomes for inchannel waterholes. Under dam scenarios, habitat-weighted changes in important flows for inchannel waterholes were greatest at node 9121011 (Figure 4-3), with a change in important flows at this single node recorded as extreme (76.9). Nodes directly downstream of the dams in scenarios B-DGPC and B-DGR resulted in moderate (5.9) and extreme (75.8) percentile change, respectively, in important flows. The changes were reduced to minor (4.1 and 2.9, respectively) for these nodes when modelled with transparent flows. This pattern reflects the combined effect of flow changes directly downstream of the dams and the benefits of providing flows to support environmental outcomes. Impacts on flow associated with dam development were greatest in subcatchments directly downstream. While these changes were significant, they may also be pessimistic due to the model set-up of removing water at the dam wall rather than routing to downstream uses. These impacts may also be mitigated to some extent by providing annual diversion commencement flow requirements or transparent flows. Climate change and water resource development for important flows for inchannel waterholes Scenario CEdry resulted in a moderate change (8.4) in important flow metrics for inchannel waterholes considering the mean across the 57 inchannel waterhole assessment nodes (Figure 4-32). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than scenarios B-DGPC (negligible; 0.4) and B-WT150P600R30E0 (negligible; 0.3). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in moderate change (8.8 and 8.7, respectively) when weighted across all inchannel waterhole assessment nodes. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160P200R30 alone. Waterholes are likely to be particularly sensitive to changes in the duration and severity of dry periods and changes in the timing of first flushes and inflows. Lower dry-season flows resulting in longer periods of low flows due to water resource development threaten to reduce the habitat value of waterholes. This can occur due to loss of waterholes within the landscape and decreases in the condition of the waterholes that remain (Department of Environment and Resource Management, 2010). This may result in a localised loss or degradation of habitat and of dependent biota (both aquatic and terrestrial) (McJannet et al., 2014) and affect community structure and food webs (Arthington et al., 2005). Where loss of waterholes occurs more frequently within the landscape, it has the potential to affect biodiversity from local to more regional scales across the catchment (James et al., 2013). The number, size and heterogeneity of waterholes in a catchment are considered important for sustaining biodiversity at larger spatial scales. Development that affects the low flows by reducing water volumes or extending the duration of low-flow periods threaten to affect the quality and persistence of waterholes within the landscape. These hydrological changes occurred predominantly under the water harvest scenarios, which were found to reduce early wet-season flows. Protecting early wet-season flows by providing annual diversion commencement flow requirements or by protecting low flows would help alleviate some of the impacts on waterholes. 4.4.3 Mangroves Mangroves forests include species of shrubs and trees that occupy a highly specialised niche within the intertidal and near-supra-littoral zones along tidal creeks, estuaries and coastlines (Duke et al., 2019; Friess et al., 2020; Layman, 2007). Mangroves in the Southern Gulf catchments are restricted to along the coastline and a narrow fringe lining both sides of connecting tidal channels and main estuaries in the Southern Gulf catchments marine region (Short, 2020). The distribution of mangroves in the Southern Gulf catchments marine region was reduced following extensive dieback of mangroves between late 2015 and early 2016, particularly along the coastline (Duke et al., 2017). Fish and crustacean communities occupy mangrove forests in the Southern Gulf catchments marine region (Staples and Vance, 1987), though community species composition is poorly studied. It is likely that most of the same species occur in these estuaries as are found elsewhere in the Gulf of Carpentaria (Blaber et al., 1995; Brewer et al., 1995). Species such as mud crab (Scylla serrata) are highly associated with the mangrove community (Robins et al., 2020), as are fish species that access the mangrove forests during periods where tidal connection permits access, presumably for shelter and food, which is similar to the east coast of Queensland (Sheaves and Johnston, 2009; Sheaves et al., 2016). While the extent of mangrove forests in this catchment area is relatively small compared to the extent of intertidal saltpans (see Merrin et al. (2024)), they still provide important linkages to coastal fisheries production when connected to the estuary, in addition to providing erosion protection, sediment accumulation and carbon sequestration services. Despite occupying saline habitats, mangroves require freshwater inputs from precipitation, groundwater or overbank inundation to thrive (Duke et al., 2017), so reduced flood flows and an increased frequency and duration of no-flow periods or other impacts on hydro-connectivity are key threats to mangroves. Asset flow dependencies analysis Mangroves were assessed at the combined end-of-system node (9100000) in the Southern Gulf catchments (see Appendix A). Locations were selected for modelling mangroves based upon habitat mapping (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for mangroves. When considering the change in important flow metrics at the end-of-system node, the hypothetical dam scenarios ranged from no measurable change (0) to a major change (16.8) under scenarios B-DGPCT and B-DGR, respectively. In contrast, water harvesting scenarios ranged from negligible (0.6) to minor (4) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (14.4) in important flow metrics for mangroves (Figure 4-33). It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric low flood pulse count (<75th percentile). For Scenario B-WT150P200R30E0, the largest contribution of change was for the metric mean winter discharge. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for mangroves. Water harvesting and changes in important flows for mangroves The hypothetical water harvesting scenarios resulted in a change at the end-of-system node for mangroves from negligible (0.6) to minor (4) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively, the latter scenario being the water harvest scenario which resulted in the largest change. The change in important flows for mangroves under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements. With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the change in flows at the end-of-system was negligible (1.3). This increased to minor (3.0) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump- start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the minor change across the assessment nodes from 4 to 2.3 (Figure 4-33). Figure 4-33 Change in mangroves flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for mangroves. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1449#yIS1 Dams and changes in important flows for mangroves Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in no measurable change (0) in important flow metrics at the end-of-system node (Figure 4-33). When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for mangroves was also not measurable (0). Scenario B-DGR, a dam on the perennial Gregory River, resulted in a larger change than Scenario B-DGPC, with a major change (16.8) in important flows at this node. This was reduced to minor (3.4) with the provision of transparent flows under Scenario B-DGRT that support the perennial nature of the river. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a major (16.8) mean change occurred across the catchment without transparent flows. This was reduced to moderate (6.2) with provision of transparent flows (Scenario B-D2T). Scenario B-D2 (with two dams) resulted in a larger flow change than a single dam on Gunpowder Creek (scenario B-DGPC), and the same change to river flows resulting from a dam on the Gregory River (Scenario B-DGR). Climate change and water resource development for important flows for mangroves Scenario CEdry resulted in a moderate change (14.4) in important flow metrics for mangroves. This indicates that the dry climate scenario led to a larger flow change than scenarios B-DGPC (negligible; 0) and B-WT150P600R30E0 (minor; 2.3). Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate (14.4) and major (15.8) change, respectively. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were similar or higher than Scenario CEdry, and higher than the water extraction scenario (B-WT160P200R30) though not as high as the effects on flow of a dam on the Gregory River (Scenario B-DGR) or two dams (Scenario B-D2) (Figure 4-33). The hydrological requirements for mangroves is complex: they are influenced by tidal inundation, rainfall, soil water content, groundwater seepage and evaporation. All of these factors influence soil salinity, which can have profound effects on mangrove growth and survival. Mangroves require access to fresh water via their roots, though many species are found at their upper salinity threshold (Robertson and Duke, 1990). Sediment delivered to the coast during flood flows helps to sustain mangrove forests, supports their seaward expansion (Asbridge et al., 2016) and increases the accumulation of carbon in sediments (Owers et al., 2022). Altered freshwater flow that reduced the likelihood of rivers spreading across coastal floodplains (see Section 2.2.2) could contribute to mangrove stress and potentially dieback similar to the events that have been recorded in the Gulf of Carpentaria (Duke et al., 2019). Scenarios that included a dam on the perennial Gregory River (i.e. B-DGR and B-D2) had major impacts on mangroves, while other scenarios showed negligible to minor flow-modification impacts (Figure 4-33). Water harvesting, at worst, had minor negative effects on freshwater provision to mangroves via flow modification during the year. One dam on a single upstream tributary had little effect as overall catchment flows were reduced by only relatively small volumes compared to the natural flow regime. The major effect of the dam on the Gregory River (Scenario B-DGR) is likely due to perennial low-level flows being modified while the high-level flood flows remain like those of Scenario A. Annual high flows are important to inundate the mangrove forests during the wet season and replenish soil water that is critical later in the year during the dry season (Duke et al., 2019). Water harvesting with low pump-start thresholds would extract water during wet-season flows, thus reducing the magnitude of flood flows at the critical period of wet- season ecological replenishment in the wet-dry tropics. In addition, reduction of the sediment loads and coastal deposition that historically maintain estuarine soils for the benefit of the mangrove community would be greater under a modified high-flow scenario (Asbridge et al., 2016). Cumulative detriment to mangrove communities resulting from flow modification due to water extraction and dam construction has been modelled in similar, nearby tropical catchments in northern Australia (Plagányi et al., 2024). Mangroves species dominate many of the creeks and rivers in the intertidal zone of the Southern Gulf catchments (Palmer and Smit, 2019; Smyth and Turner, 2019) where freshwater provided by inundation is an important process for supporting mangrove species. Changes in flow regimes leading to a reduction in the area of mangrove habitat inundated due to a future drying climate and water resource development would lead to a reduction in mangroves, affecting the available habitat for many species, including birds, fish, prawns, mud crabs and reptiles for which mangroves provide both foraging and breeding habitat (see Merrin et al. (2024)). Mangroves also provide a range of ecosystem services, which would be reduced under a future drying climate and water resource development. These include shoreline stabilisation, carbon capture and storage, storm surge protection and reducing nutrient loads and suspended sediments, which is important for water quality (Palmer and Smit, 2019). 4.4.4 Saltpans and salt flats Saltpans and salt flats are extensive intertidal areas devoid of marine plants and located between mangrove and saltmarsh meadows within the uppermost intertidal zone. Saltpans and salt flats occur across much of northern Australia. Despite their infrequent inundation, when they are covered by the tide saltpans and salt flats provide habitat for some estuarine fish species, such as barramundi (Russell and Garrett, 1983), as well as other species, such as metapenaeid shrimps (Bayliss et al., 2014). In addition, saltpans and salt flats on the Queensland and NT coasts of the southern Gulf of Carpentaria provide important resting and feeding grounds for migratory shorebirds, which number in the tens of thousands (Palmer and Smit, 2019). During the wet season, king tides, heavy rainfall and overbank inundation may create months-long ephemeral habitats for fish and crustaceans (Russell and Garrett, 1985; Russell and Garrett, 1983) and stimulate primary production by the microphytobenthos that are senescent in the dry season and encrust the saltpan soils in the wet season (Burford et al., 2016). Saltpans and salt flats also provide habitat for a range of benthic infauna (Dias et al., 2014), which are an important food source for high-order consumers such as shorebird species that use these habitats as feeding areas during their migration, which can include long flights to Asia (Cotin et al., 2011; Lei et al., 2018; Rocha et al., 2017). In the Southern Gulf catchments, saltpans and salt flats commonly occur behind tide-dominated beaches and may extend 30 to 50 km inland (Short, 2020). They are mostly restricted to a tidal inundation area on the landward side of the mangroves that line the main river channel, but they also occur adjacent to Buffalo and Sweet swamps. Saltpans and salt flats support many of the species and groups reported as biota assets in this report (e.g. see sections 4.1.1 for barramundi and 4.2.3 for shorebirds), particularly during high-flow events that connect the saltpans and salt flats to the seascape. In addition, annual inundation of salt flats during overbank flows invigorates the senescent algal crust that covers the southern Gulf of Carpentaria estuarine/salt flat complex, contributing an extra 13% to ecosystem primary productivity during high-level floods (Burford et al., 2016). Despite occupying supra-tidal habitats, saltpans and salt flats require freshwater inputs from precipitation, groundwater or overbank inundation for cyanobacteria and marine plants (including saltmarsh species) to thrive (Duke et al., 2017). Hence, reduced flood flows and an increased frequency and duration of no-flow periods are key threats to assets that require these habitats. Asset flow dependencies analysis Salt flats and saltpans were assessed at the combined end-of-system node (9100000) in the Southern Gulf catchments. Locations were selected for modelling salt flats and saltpans based upon habitat mapping (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for saltpans and salt flats. When considering the flow change in important metrics, the hypothetical dam scenarios ranged from no measurable change (0) to a moderate change (5.7) under scenarios B-DGPCT and B-DGR, respectively. In contrast, water harvesting scenarios ranged from negligible (0.8) to moderate (5.8) under scenarios B- WT50P600R30E250 and B-WT150P200R30E0, respectively. Scenario CEdry resulted in a moderate change (9.3) in important flow metrics for saltpans and salt flats. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric mean November discharge. For scenario B-WT150P200R30E0, the largest contribution of change was also for the metric mean November discharge. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for saltpans and salt flats. Water harvesting and changes in important flows for saltpans and salt flats The hypothetical water harvesting scenarios resulted in a flow change for saltpans and salt flats from negligible (0.8) to moderate (5.8) under scenarios B-WT50P600R30E250 and B-WT150P200R30E0, respectively. The change in important flows for saltpans and salt flats under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-34). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the change in flows were negligible (1.8). This increased to minor (4.1) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump- start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) reduced the change across the assessment nodes from moderate (5.8) to minor (3.3) (Figure 4-34). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, maintaining important overbank flows that inundate saltpans and salt flats and enhance coastal primary productivity, and increasing the pump-start threshold protects the low flows that are important for salt flat ecology. Figure 4-34 Change in saltpans and salt flats flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for saltpans and salt flats. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1469#yIS1 Dams and changes in important flows for saltpans and salt flats Under the dam scenarios, Scenario B-DGPC resulted in a no measurable change (0) in important flow metrics for saltpans and salt flats (Figure 4-34Figure 4-34). When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for saltpans and salt flats remained not measurable (0), suggesting that this scenario may have minimal impact on the existing structure and function of saltpans and salt flats. The moderate change (5.7) under Scenario B-DGR, larger than the change under Scenario B-DGPC, could lead to alterations in the frequency and duration of inundation events, potentially disrupting salt accumulation and evaporation and so altering the habitat and making it less suitable for specialised species. The change was reduced to minor (4.8) with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a moderate mean change (5.7) occurred across the catchment without transparent flows. This was reduced but still moderate (5.3) with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a larger mean flow change than either of the single-dam scenarios. Climate change and water resource development for important flows for saltpans and salt flats Scenario CEdry resulted in a moderate change (9.3) in important flow metrics for saltpans and salt flats considering the change at the end-of-system node (Figure 4-34). This indicates that the dry climate scenario led to a larger change than scenarios B-DGPC (negligible; 0) and B-WT150P600R30E0 (minor; 3.3). Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in moderate change (9.3 and 11.4, respectively). This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT160P200R30 alone. The ecological impacts of dams and river regulation on saltpans and salt flats can be numerous. Most importantly, large rainfall events can be captured, preventing flood pulses from moving down the catchment and reaching dynamic estuaries and low-lying coastal areas, and the resulting reduced flows can prevent water from flowing overbank onto low-lying habitats. Reduced frequency and depth of overbank flows would impair carbon and nitrogen inputs to the coastal ecosystem as wetting of salt flat algal crusts would be limited compared to the historical trend (Burford et al., 2016). Loss of connectivity to coastal floodplain areas, including low-lying saltpans and salt flats, would result in the reduction or loss of coastal wetland habitats (Lei et al., 2018; Velasquez, 1992). Wet-season rainfall and high flows recharge soil water and groundwater in Gulf of Carpentaria coastal ecosystems (Duke et al., 2019). In addition, sediment replenishment delivered to the coast during flood flows sustains coastal habitats, preventing erosion and degradation of important estuarine and adjacent habitats (Asbridge et al., 2016) and the accumulation of carbon in deposition sediments (Owers et al., 2022). 4.4.5 Seagrasses Seagrasses are marine flowering plants that provide valuable food resources and habitat to a diverse community of animals, including invertebrates, fish, sea turtles, dugongs and many other marine organisms. In northern Australia, 15 species of seagrass occupy tidal and subtidal reaches of rivers, coastal, reef and deep-water habitats (Carruthers et al., 2002). Of those, seven seagrass species have been recorded in the Southern Gulf catchments, all of which are listed as ‘special least concern’ under the Queensland Nature Conservation Act 1992. The distribution of seagrasses in the Gulf of Carpentaria is fragmented, characterised by aggregated seagrass patches with many bare areas between them (see Merrin et al. (2024)). Relatively few meadows have continuous seagrass cover, although these tended to also be large meadows (Roelofs et al., 2005). Seagrasses are considered one of the most valuable ecosystems globally due to their provision of key ecological services (Duarte et al., 2013), such as carbon sinks (Fourqurean et al., 2012), fisheries habitat (Unsworth, 2019), nutrient cycling, enhanced biodiversity and sediment stabilisation (Orth, 2006). Seagrasses are generally intolerant of fresh water for more than brief periods of exposure (Adams and Bate, 1994; Collier et al., 2014) and can be harmed by direct exposure to flood plumes (Collier et al., 2014). Sedimentation and high levels of turbidity associated with large flood discharges can cause a reduction in seagrass extent (Turschwell et al., 2021). Despite seagrasses occupying coastal habitats, the key threats to seagrasses are reduction in seagrass extent or changes in species community composition due to intolerance of freshwater plumes during large flood events and smothering due to high turbidity or sedimentation during high-discharge flood events. In the Southern Gulf catchments, seagrasses occur in the littoral zones around Mornington and Sweers islands and on the Gulf of Carpentaria coastline west of Mornington Island (Coles and Lee Long, 1985; Poiner et al., 1987). Asset flow dependencies analysis Seagrasses were assessed in the marine region of the Southern Gulf catchments with contributing flows from the combined end-of-system node (9100000). Locations were selected for modelling seagrasses based upon habitat maps (see Merrin et al. (2024)). It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. Under scenario D2, the largest contributing change in important flow dependencies was for the metric annual maxima of 30-day means of daily discharge. For scenario B-WT150P200R30E0, the largest contribution of change was for the metric maximum daily flows. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for seagrasses. Water harvesting and changes in important flows for seagrasses The change in important flow dependencies for seagrasses under water harvesting varied depending on extraction target volumes, pump-start thresholds, pump rates and end-of-system targets (Figure 4-35). With a low extraction target volume of 50 GL under Scenario BWT50P600R30E0, the change in flow dependencies was negligible (0.4). This increased slightly to (0.9) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario BWT150P200R30E0) to 600 ML/day (Scenario BWT150P600R30E0) lead to a slight increase in the negligible change from 0.4 to 0.8 (Figure 4-35). Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year. Figure 4-35 Change in seagrasses flow dependencies by scenario for the end-of-system node Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for seagrasses. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1486#yIS1 Dams and changes in important flows for seagrasses Under the dam scenarios, Scenario B-DGPC (without transparent flows) resulted in no measurable change (0) in key flow metrics at the seagrass end-of-system node. When transparent flows were implemented to support environmental outcomes (Scenario B-DGPCT), the associated change in the important flows for seagrasses remained not measurable (0). Scenario B-DGR resulted in a larger change than Scenario B-DGPC, with a negligible change (0.9) in important flows averaged across the assessment nodes. This increased to 1.3 but remained negligible with the provision of transparent flows under Scenario B-DGRT. Under Scenario B-D2 (which includes both the B-DGPC and B-DGR dams), a negligible change (0.9) occurred without transparent flows. This increased to 1.5 but remained negligible with provision of transparent flows. Scenario B-D2 (with two dams) resulted in a larger flow change than Scenario B-DGPCT as reduced flow levels lessen freshwater incursion within coastal habitats during the wet season. River regulation can alter the flow regime and affect water quality. Seagrasses, which vary in their tolerance to extended periods of freshwater exposure (Adams and Bate, 1994), are generally vulnerable to harm from direct exposure to flood plumes (Collier et al., 2014) and sedimentation (Turschwell et al., 2021). Climate change and water resource development for important flows for seagrasses Scenario CEdry resulted in a moderate change (7.1) in important flow metrics for seagrasses at the seagrass assessment node (Figure 4-35). This indicates that the dry climate scenario led to a larger change than scenarios B-DGPC (no measurable change; 0) and B-WT150P600R30E0 (negligible; 0.8). However, considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 resulted in moderate change (both 7.1) at the assessed seagrass node. This shows that the combined changes of scenarios D-DGPC or DdryWT150P600R30E0 were equal to Scenario CEdry and higher than Scenario B-D2. The impact of a dry climate on seagrasses depends on the reduction of large flood events. Globally, river regulation, land use change and climate change have been identified as threatening processes that affect seagrass species and communities (Turschwell et al., 2021). Within the Southern Gulf catchments, modelled flow regime change associated with river regulation on seagrasses has been assessed as negligible to minor, depending on the water development scenario. River regulation can change the flow regime and the resulting water quality. Seagrasses are generally intolerant of extended periods of fresh water (although the degree of tolerance varies by species) (Adams and Bate, 1994) and can be harmed by direct exposure to flood plumes (Collier et al., 2014) and sedimentation (Turschwell et al., 2021). The effect of a dry climate on seagrasses depends also on the potential for a reduction in large flood events that are associated with scouring, sedimentation and turbidity within the freshwater flood plume. 4.4.6 Surface-water-dependent vegetation Across much of the Southern Gulf catchments, terrestrial vegetation survives on water derived from local rainfall that recharges soils during the wet season and can be accessed by the root systems within unsaturated soils throughout the year. Terrestrial vegetation that receives extra water in addition to local rainfall (e.g. recharge from flood waters or by accessing shallow groundwater (Doody et al., 2017), often provides a lush green and productive forest ecosystem with high diversity and dense tree cover within an otherwise drier or more sparsely vegetated savanna environment (Pettit et al., 2016). Terrestrial vegetation communities that receive and are supported by surface water in addition to incident rainfall are considered surface-water- dependent vegetation in this report. Habitats of surface-water-dependent vegetation often occur along rivers and floodplains, fringing wetlands and springs, and they may also have access to groundwater within reach of the root system. Surface-water-dependent vegetation can be highly sensitive to changes in flooding regime (inundation extent, depth, duration and frequency; see Roberts and Marston (2011)). There may be a lagged response in vegetation condition to reduced surface water availability (see Broich et al. (2018); Zheng et al. (2024)) because water stored in soil or local aquifers can provide a buffer for maintaining vegetation condition. However, if these sources are not regularly topped up by flood recharge, less water will be available to support floodplain vegetation during dry periods. Furthermore surface-water-dependent vegetation may need floodwater inundation to support growth, flowering and fruiting, germination, and successful establishment of new saplings to maintain the diversity of ecosystem species and their functions and services (Roberts and Marston, 2011). In northern Australia, surface-water-dependent vegetation provides food and habitat for high levels of biodiversity (e.g. for migratory waterbirds, honeyeaters, flying foxes and crocodiles; Queensland Department of Environment and Science, 2013; Fukuda and Cuff, 2013; Williams, 2011), plays a role in nutrient cycling and provides buffering against erosion (Petsch et al., 2023). The key threats to surface-water-dependent vegetation are associated with changes in flood regimes (inundation extent, depth, duration and frequency) that support vegetation survival and growth, flowering and fruiting, and germination and establishment of new individuals. Asset flow dependencies analysis The flow dependencies analysis investigates flow parameters likely to affect surface-water- dependent vegetation. However, some of this vegetation may also be groundwater dependent, and the flow dependencies modelling does not explicitly investigate the potential impacts of captured recharge (reduction in recharge to local aquifers due to surface water regulation). Where surface water regulation influences the inundation extent, duration or frequency, it is also likely to alter the local aquifer recharge and therefore groundwater availability to vegetation during dry periods. While metrics of flow magnitude, duration and frequency are included in the flow dependencies analysis, extent of inundation is not explicitly modelled. Therefore, the impacts of dams and water harvesting on captured recharge are not fully accounted for within this analysis alone. Nevertheless, the flow dependencies analysis is a useful preliminary investigation of potential impacts of dams, water harvesting and climate on important flow components of surface-water-dependent vegetation, with the caveat that not all recharge mechanisms are fully incorporated. Surface-water-dependent vegetation was assessed across a total of 3948 km of river reaches in the Southern Gulf catchments with contributing flows from 62 model nodes (see Appendix A). Locations were selected for modelling surface-water-dependent vegetation based upon habitat maps and evaluation of knowledge (see Merrin et al. (2024)). Hypothetical water resource development in the Southern Gulf catchments led to varying levels of change in key flow metrics important for surface-water-dependent vegetation. When considering the mean weighted change in important flow metrics across all 62 surface-water-dependent vegetation analysis nodes, the hypothetical dam scenarios resulted in minor change (2.1 and 4.4) in flow under scenarios B-DGPCT and B-D2T, respectively. Similarly, water harvesting scenarios ranged from negligible (1.7) to minor (3.0) under scenarios B-WT50P600R30E0 and B-WT300P600R30E150, respectively. Scenario CEdry resulted in a moderate change (14.6) in important flow metrics for surface-water-dependent vegetation. It is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. The level of flow regime change associated with dam construction, water harvesting and climate scenarios varied due to the differing spatial patterns of flow regime change and the distribution of important habitat for surface-water-dependent vegetation (Figure 4-36). Under scenario D2, the largest contributing change in important flow dependencies was for the metric high flow pulse duration single spell (10th percentile) at node 9130030. For scenario B-WT150P200R30E0, the largest contribution of change was also for the metric high flow pulse duration single spell (10th percentile) but at node 9130061. See Appendix C for important metrics and Appendix D for the most changed metrics under each scenario for each asset, and Merrin et al. (2024) for descriptions of flow-ecology relationships for surface-water-dependent vegetation. Figure 4-36 Spatial heatmap of habitat-weighted changes in flow for surface-water-dependent vegetation, considering the assets important locations across the catchment Scenarios are: (a) B-WT150P600R30E0, (b) B-WT150P600R30E150, (c) B-WT300P600R30E0, (d) B-D2, (e) CEdry and (f) D-WT150P600R30E0. River shading indicates the level of flow change of important metrics for surface-water-dependent vegetation weighted by the habitat value of each reach. P1503#yIS1 Water harvesting and changes in important flows for surface-water-dependent vegetation The hypothetical water harvesting scenarios resulted in a mean change across surface-water- dependent vegetation assessment nodes from negligible (1.7) to minor (3) under scenarios B-WT50P600R30E0 and B-WT300P600R30E150, respectively. The change in important flows for floodplain and riparian vegetation under water harvesting showed little variation associated with extraction target volumes, pump-start thresholds, pump rates and annual diversion commencement flow requirements (Figure 4-37). With a low extraction target volume of 50 GL under Scenario B-WT50P600R30E0, the mean weighted change in flows across the catchment was negligible (1.7). It increased to minor (3) with an extraction target volume of 300 GL under Scenario B-WT300P600R30E0. The pump-start threshold is important for protecting low flows by preventing pumping when the river is below this threshold. With a target extraction volume of 150 GL, increasing the pump-start threshold from 200 ML/day (Scenario B-WT150P200R30E0) to 600 ML/day (Scenario B-WT150P600R30E0) marginally increased the minor change across the assessment nodes from 2.2 to 2.3 (Figure 4-37). However, all dam and water harvesting scenarios resulted in local extreme changes in important flow metrics at 9130061 (upstream of the Gunpowder Creek dam). This may largely be due to the uptake of currently held but under-utilised water licences in the area. Further investigation would be required to discover how best to protect surface-water-dependent vegetation at this location. Measures to protect important parts of the flow regime can support ecology. For example, reducing the extraction target volume of water extracted in any water year, and increasing the pump-start threshold protects the low flows that are important for freshwater-dependent ecosystems. However, high flows that inundate greater areas are likely to be more crucial for surface-water-dependent vegetation. Figure 4-37 Habitat-weighted change in surface-water-dependent vegetation flow dependencies by scenario across model nodes Colour intensity represents the level of change occurring in important flow metrics, expressed as percentile change from the historical conditions and weighted by the importance of each reach for surface-water-dependent vegetation. Equivalent colour intensity (i.e. corresponding to the asset flow dependency change value) for the Ord River below Lake Kununurra is shown in the bottom right. Scenarios are ordered on the left axis by the magnitude of change with the mean change across nodes shown on the right axis. Horizontal grey bars and numbers correspond to the mean change across all model node locations. Only the 30 highest impact nodes are shown (x-axis). Results under Scenario A corresponding to changes in asset flow dependency for the lowest 30-year (A lowest 30), 50-year (A lowest 50) and 70-year (A lowest 70) time periods provide a reference for the modelled changes under different hypothetical development and projected future climate scenarios. AN-AE corresponds to the change in asset flow dependency in the Nicholson and Leichhardt catchments that has already occurred since European settlement. P1508#yIS1 Dams and changes in important flows for surface-water-dependent vegetation All dam scenarios, whether single or combined, and with or without transparent flows, resulted in a minor percentile change (2.1 to 4.4) in important flow metrics when averaged across the 62 surface-water-dependent vegetation assessment nodes. Scenario B-DGR (3.3) caused a higher mean change in important flow metrics for surface-water-dependent vegetation than Scenario B-DGPC (2.4), and the impact of Scenario B-D2 (4.3) was slightly higher again. However, local changes in important flow metrics for surface-water-dependent vegetation were extreme (49.9) immediately downstream of the potential Gunpowder Creek dam (9130030; Figure 4-37) and major (22.4) downstream of the potential Gregory River dam (9121050). Scenario B-DGPCT, reduced the change in important flow metrics for surface-water-dependent vegetation but it remained extreme (38.6) downstream of Gunpowder dam and minor (2.1) when averaged across all assessment nodes. Under Scenario B-DGRT, the implementation of transparent flows increased the change in important flow metrics for surface-water-dependent vegetation, but it remained major (28.7) downstream of the Gregory River dam and minor (3.6) when averaged across all assessment nodes. Likewise, transparent flows under Scenario B-D2T caused a slight increase (minor; 4.4) in the change of important flow metrics for surface-water-dependent vegetation when averaged across the assessment nodes. However, locally the change decreased downstream of Gunpowder Creek dam (9130030) but remained extreme, while it increased downstream of the Gregory River dam (9121050) but remained major (Figure 4-35). At the Gunpowder Creek dam, the decrease in the change of flows associated with implementation of transparent flows demonstrates the importance of providing flows to support environmental outcomes for surface-water-dependent vegetation. In contrast, the increase in the change of flows at Gregory Creek dam and under the combined dam scenario highlights the importance of high flows for inundating surface-water-dependent-vegetation. It also shows that where dam management decreases the extent of surface water inundation it is also likely to decrease the extent of surface-water-dependent vegetation. Climate change and water resource development for important flows for surface-water- dependent vegetation Scenario CEdry resulted in a moderate change (14.6) in important flow metrics for surface-water- dependent vegetation considering the mean across the 62 surface-water-dependent vegetation assessment nodes (Figure 4-37). This indicates that the dry climate scenario led to a larger mean change across all catchment nodes than all dam and water harvesting scenarios (e.g. B-DGPC (minor; 2.4) and B-WT150P600R30E0 (minor; 2.3)). However, it is important to note that local changes in flows under some water resource development scenarios can be considerably higher than the catchment means. For example, the impact of dams caused locally greater changes in important flow metrics at 9121050 (Gregory River dam) and 9130061 (Gunpowder Creek dam) than CEdry. Considering the combined impacts on flow associated with climate change and water resource development, scenarios D-DGPC and D-WT150P600R30E0 both resulted in major change (16 and 15.8, respectively) when weighted across all surface-water-dependent vegetation assessment nodes. This shows that the combined changes of scenarios D-DGPC or D-WT150P600R30E0 were higher than Scenario CEdry or either of scenarios B-D2 and B-WT150P200R30 alone. A key threat to surface-water-dependent vegetation in the Southern Gulf catchments is change to the dynamics of water availability. The development of water resources, including dam construction and water harvesting in combination with climate change, has the potential to influence floodplain inundation extent, timing and duration, which affects recharge and discharge from floodplains and the availability of suitable quality water to floodplain vegetation at critical times. The specific surface water flow dependencies of surface-water-dependent vegetation are highly dependent on local site conditions (climate, soils, topography, groundwater) and vegetation type. Some riparian vegetation is adapted to inundation for several months of each year (Queensland Department of Environment and Science, 2013), whereas some floodplain vegetation may only require inundation for 2 months every 5 years (Wen et al., 2009). Dam operations or water harvesting could potentially be managed to mimic the natural timing, frequency, duration and extent of surface water flows that naturally inundate surface-water- dependent vegetation habitats and recharge local aquifers that sustain some surface-water- dependent vegetation during dry times. However, this flow requirement analysis shows major to extreme changes in the important surface water flow components that support surface-water- dependent vegetation downstream of instream dams and moderate widespread changes in response to water harvesting scenarios. It also shows moderate to extreme changes catchment- wide to the important surface water flow components that support surface-water-dependent vegetation when paired with a drying climate. High flows are essential for widespread delivery of water to floodplain vegetation, and low flows are key for maintaining riparian vegetation. The demand for water for irrigated agriculture is likely to coincide with times when surface-water- dependent vegetation is most likely to experience water deficit. Furthermore, overall reduced inchannel flows may enhance drainage of alluvial aquifers, potentially reducing availability of both groundwater and surface water to surface-water-dependent vegetation at critical times. Vegetation that experiences water stress might first exhibit loss of ecosystem function (e.g. reduced flowering and seed dispersal), but water stress could ultimately result in local dieback of higher-water-need vegetation and a transition to vegetation resilient to drier conditions. This transition may take years depending on the magnitude and rate of change in water availability and the resilience of the vegetation to water stress (Mitchell et al., 2016; Van Mantgem et al., 2009). 5 Synthesis Context of the catchments The Southern Gulf catchments encompass rich freshwater and marine environments that contain important and diverse species and habitats including at least 170 species of fish, 150 species of waterbirds, 30 species of aquatic and semi-aquatic reptiles, 60 species of amphibians, and 100 macroinvertebrate families. The region’s freshwater systems are particularly rich in species adapted to the highly seasonal flow regimes of the wet-dry tropics. Several species in the area are listed as critically endangered, endangered, or vulnerable under the Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) and by state conservation systems. Notable among these are the freshwater or largetooth sawfish (Pristis pristis), which is classified as vulnerable, and the Gulf snapping turtle (Elseya lavarackorum), listed as endangered. The Southern Gulf catchments also serve as crucial stopover habitats for migratory shorebird species like the eastern curlew (Numenius madagascariensis), classified as critically endangered, and the Australian painted snipe (Rostratula australis), classified as endangered. The Southern Gulf catchments encompass rich marine and estuarine environments that feature extensive intertidal flats and estuarine communities, including mangroves, salt flats, and seagrass habitats. The catchments contain 13 nationally significant DIWA listed wetlands. These species and habitats are of high conservation, social and economic importance. Northern Australia is characterised by large seasonal variability with significant year-to-year variability in flows. Many rivers only flow during the wet season, and freshwater and marine ecosystems have adapted to this seasonal variability. Water resource development can provide a range of threating processes including habitat modification, invasive species, habitat fragmentation and changes to water quality, flow and fire regimes. This report explored the changes in flow regimes associated with scenarios of water resource developing and the change in important flow dependencies of prioritised ecological assets. For the ecology assessment, assets spanned freshwater, marine and terrestrial ecosystems and included species, species groups and habitats. Understanding potential change This report should be read in conjunction with the Southern Gulf Water Resource Assessment Ecological Assets Description report by Merrin et al. (2024) which also provides a qualitative discussion other threatening processes on assets that can occur as a result of, or in synergy with, water resource development. Merrin et al. (2024) documents the asset ecology including flow relationships and dependencies and the distribution of each asset across the catchment. The impacts associated with loss of habitat and connectivity by the creation of instream dams is discussed in Yang et al. 2024 and the impacts on changes to water quality are discussed in Motson et al. (2024). • Different ecological assets have different flow dependencies. These were modelled using a suite of asset-specific hydrometrics based upon the flow-ecology of each asset. Change in important flow dependencies were modelled as the percentile change of the scenario median hydrometrics relative to the historical distribution and are hence benchmarked against the variability occurring in the historical flow regime. • Ecological assets have different distributions across the catchments. Water resource development in different parts of the catchment will not affect assets equally as asset occurrence differs across the catchments. The importance of habitat across the catchments were quantified using a combination of species distribution modelling, observed distribution, habitat maps and expert knowledge. The asset flow dependencies were modelled in the important reaches for each asset downstream of river system model nodes to capture changes in flow affecting the assets’ important locations. • Different scenarios of water resource development resulted in different volumetric, temporal and spatial patterns of change in flow regimes. Ecological assets are adapted to, or occur as a result of, the flow regime. Changes to the flow regime will result in changes to the ecology of the system. While most changes from the current ecological function of the system are considered detrimental, there are some species that may benefit or new habitats conditions that arise because of changes in flow. Ecology is complex, and difficult to predict into novel conditions. • Ecological systems are more than the sum of their parts. Systems have complex interactions that occur across different temporal and special scales. Important habitats or functions may be nested within the landscape, for example, refuge waterholes are important for providing a source for recolonisation of the surrounding system following dry conditions and the loss of waterholes in a location may have larger impacts beyond that of the individual sites, while a river reach may be important for connecting vast riverscapes. Individuals of a species occur within a population, and populations within communities. • Aquatic systems do not occur in isolation. With water resource development comes land-use change and intensification. This can result in a suite of synergistic and co-occurring threatening processes including changes to water quality from increased nutrient loads and changes to runoff, increased threat from invasive species associated with higher likelihood of both introduction and establishment, changes to connectivity associated with roads, culverts, and instream structures, as well as changes to fire regimes. Climate change adds additional uncertainty and risk associated with drying conditions and changes in rainfall patterns. • Non-linear responses and trade-offs occur in natural systems. The outcomes of any potential water resource development do not affect all assets equally, nor ecological assets equally across the catchments. The approach to analysis is generalisable across the catchments, however thresholds are likely to occur in ecology. Wetlands may have a commence-to-fill, where once river flows exceeds this level the wetland is inundated. Surface-water-dependent vegetation along riparian corridors downstream of dams may benefit from persistent watering associated with releases from dams, however watering of vegetation higher on the floodplain may be reduced due to the capture of flood flows into the dam. Flow-ecology The flow regime in northern Australia is highly variable with large seasonal and inter-year variability. The natural flow regime is important for supporting species, habitats and a range of ecosystem functions. Species life-histories are often intricately linked to specific flow conditions considering the magnitude, timing and frequency of flow events. The ecological assets considered in this report represent a range of flow dependencies and have different spatial patterns of occurrence across the catchments. • High flows provide a range of important functions including providing connectivity for movement, increasing productivity and nutrient exchange, providing cues for spawning and migration, and wetting habitat and supporting vegetation growth and persistence. The magnitude, duration and timing of high flows is important in ecological systems. • Low flows are also an important component of the flow regime with many species adapted to these conditions. Persistent waterholes provide important refuge habitat from environmental conditions and higher levels of predation that may occur in connected rivers. For many species refuge waterholes function as a source for recolonisation during the wet season. Persistent low flows can help support suitable habitat conditions including thermal and water quality for species in connected rivers and in supporting riparian vegetation and movement and provide a source of water within the broader landscape. • The timing of flow events is important in supporting life-cycle processes including breeding and migration cues for aquatic species. The timing of flood events and the associated increase in productivity supports function in the river channel and connected marine environments. Water resource development In the Southern Gulf catchments, changes associated with water resource development demonstrated that: • Different ecological assets had variable sensitivity to flow change. This depended on each assets flow dependencies, location in the catchment, and the type and size of the development considered including the volumes of water extracted, the timing of water extraction and the volume of water that is passing through the river. Outcomes for ecology is more than about just the volume of water extracted. • Mitigation measures including providing an annual diversion commencement flow requirement, pump commencement thresholds, extraction target volumes and pump rates for water harvesting, and transparent flows for dams often provided effective in reducing the effects of flow regime change. • Interannual variability in the flow regime across the catchments was larger than the mean change associated with water resource development – but not consistently. Some scenarios resulted in a level of change greater than what was observed in historical flows. This was usually confined to sites directly downstream of dams or could be associated with shifts from a perennial system to one that is ephemeral. • While the effects of water resource development depend upon the scale, location and type of development, effects are typically reduced with further distance downstream of the last development. However, the effects of change can have consequences considerable distances downstream and into the near-shore and marine environments. Water harvesting • Water harvesting affected the turtles, prawns and other species group, the fish, sharks and rays and the flow dependent habitats. • For water harvesting, measures to mitigate the risks of extraction include limiting the system target thereby reducing extraction across the catchment, providing a pump-start threshold by limiting pumping of water from the river during periods of low river flows, providing an annual diversion commencement flow requirement for a volume of water to pass through the last node in the system before pumping is allowed, and limiting the pump rate that water can be extracted from the river provide for better environmental outcomes than without these measures. • These measures improve environmental outcomes, particularly for sensitive species like fish, sharks, rays, and freshwater-dependent habitats, by reducing flow dependencies and retaining early wet-season flows. Higher extraction volumes still lead to increased ecological changes but adjusting thresholds and limiting extraction rates can mitigate these impacts, especially when system targets are kept low. • Higher extraction volumes still lead to increased ecological changes but adjusting thresholds and limiting extraction rates can mitigate these impacts, especially when system targets are kept low. Instream dams • In the Southern Gulf catchments, potential dams and their associated changes in flows resulted in larger changes for mullet, threadfin and prawn species considering the mean change across the catchment. Site impacts were often highest directly downstream of dams with often extreme changes in flow dependencies for assets including inchannel waterholes, cryptic wading waterbirds, sawfish and grunter. These ecological assets often-had flow dependencies for low flow requirements or periods of stable flows. Areas further downstream of potential dams have contributions from unimpacted tributaries thereby reducing change of the flow regime. Freshwater assets typically had distributions that included many unimpacted parts of the Assessment region. • Construction of dams results in loss of connectivity along the river (longitudinal connectivity) due to instream barries and changes in flow, as well as loss of connectivity between the river and its surrounding floodplains (lateral connectivity) due to changes in flow. These changes limit species movement between habitats. Many species need movement between freshwater and marine environments or different habitats within the catchment. Dams in headwater catchments typically result in smaller changes to longitudinal connectivity than compared to dams closer to the end-of-system. • Creation of dams inundate terrestrial and stream habitat resulting in the creation of new habitat conditions associated with the impoundment. In some cases, these new habitats may provide resources for some species such as waterbirds, but other habitats are degraded or lost impacting the species that depend upon them. Persistent flows from storages change downstream habitat and result in modification of ecological communities and potential loss of existing species. • Providing transparent flows- inflows let to pass the dam wall for environmental purposes- provided improvements for most assets compared to without these. Particularly strong improvements in flow dependencies for fish and waterbirds occurred. Climate change and water resource development • Climate change had larger potential mean change in flows across the catchments than compared to many scenarios of water resource development. 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The species distribution models were developed using Random Forests, Generalised Linear Models (GLMs), and Maxent algorithms (see Merrin et al., 2024). These models were applied to a 2.5 km buffer surrounding the rivers within the Southern Gulf catchments to quantify habitat suitability. The change in the flow dependencies was weighted by habitat suitability for each asset between the river system model nodes of each river reach. Flow regime change for each asset is assessed within the downstream subcatchments from the river system model nodes considering the significance and presence of assets within each subcatchment. Further information on the distribution of species and habitats and the rational for node selection for each species is provided in Merrin et al. (2024). Apx Table A-1 River system model nodes used for each of the fish, sharks and rays The combined end-of-system river system model node 9100000 is the designated end-of-system node unless otherwise stated. This EOS node combined flows from the Nicholson (9121090 and 9129040) and the Leichhardt (9130071) outlets. F= Freshwater; M= Marine and F/M = Freshwater and Marine NODE ID BARRAMUNDI (F/M) CATFISH (F) GRUNTER (F) MULLET (F) SAWFISH (F/M) THREADFIN (M) 9100000 100 0 0 100 100 100 9121010 36.5 100 46.8 0 63.7 0 9121011 61.2 73.9 55.5 0 80 0 9121012 53.0 80.7 48.1 0 93.8 0 9121013 46.4 53.8 49.7 0 83.5 0 9121014 53.0 81.0 48.0 0 93.7 0 9121015 79.5 64.5 63.5 0 82.8 0 9121016 79.8 64.4 63.9 0 82.2 0 9121020 48.1 74.3 52.0 0 68.8 0 9121030 46.4 78.8 37.6 0 77.9 0 9121031 57.6 76.5 67.9 0 62.7 0 9121032 43.8 76.8 44.8 0 67.0 0 9121033 56.1 83.7 53.8 0 82.4 0 9121034 69.6 72.2 51.9 0 84.5 0 9121035 69.5 71.9 51.7 0 84.3 0 9121070 52.7 74.1 57.4 0 69.5 0 9121071 48.4 57.6 63.0 0 57.3 0 9121072 55.4 73.4 70.8 0 66.3 0 9121073 68.2 80.8 100 0 92.8 0 9121074 68.2 80.8 100 0 92.8 0 NODE ID BARRAMUNDI (F/M) CATFISH (F) GRUNTER (F) MULLET (F) SAWFISH (F/M) THREADFIN (M) 9121040 55.9 72.5 50 0 78.3 0 9121041 70.4 77.0 49.2 0 89.5 0 9121050 54.1 69.3 42.4 0 75.7 0 9121051 46.1 63.6 45.8 0 75.8 0 9121052 49.0 71.7 46.7 0 70.8 0 9121053 53.5 79.5 59.8 0 96.5 0 9121060 58.6 76.5 63.6 0 84.2 0 9121061 70.3 77.0 49.2 0 89.5 0 Apx TableA-2River systemmodel nodes used for eachofwaterbirds The combined end-of-system river system model node 9100000is the designated end-of-system node unlessotherwise stated.9100000. This EOS node combined flows from the Nicholson (9121090 and 9129040) and theLeichhardt (9130071)outlets.F= Freshwater; M= Marine and F/M = Freshwater and Marine. NODE IDCOLONIAL AND SEMICOLONIAL WADERS(F) CRYPTIC WADERS* (F)SHOREBIRDS(F/M)SWIMMERS, DIVERS ANDGRAZERS(F) 9100000 0 0 0 0 9121010 100 100 24.7 36.9 9121011 78.8 0 22.3 49.9 9121012 94.4 100 22.3 51.8 9121013 75.1 100 13.7 65.8 9121014 94.7 100 22.4 51.8 9121015 88.3 100 76.5 84.0 9121016 87.9 100 76.6 84.5 9121020 61.8 100 22.0 31.9 9121030 70.2 0 16.5 39.5 9121031 61.4 100 39.0 46.4 9121032 68.6 0 18.8 38.0 9121033 80.5 100 23.3 36.6 9121034 80.3 0 55.4 72.7 9121035 79.8 0 55.2 73.3 9121040 76.6 0 18.9 50.5 9121041 77.6 100 46.1 69.5 9121050 65.7 100 28.3 31.6 9121051 63.3 100 29.7 23.1 9121052 87.8 100 18.2 43.2 9121053 65.8 100 19.4 45.1 9121060 67.0 100 30.2 62.4 9121061 77.6 100 46.1 69.5 9121070 82.4 0 26.0 58.1 9121071 71.1 100 20 49.7 9121072 68.3 0 33.6 56.1 9121073 66.9 0 40 51.7 9121074 66.9 0 40 51.7 9121075 68.4 0 33.6 56.1 9121080 46.6 100 20.5 30.9 9121100 62.0 100 19.7 39.6 9121101 67.1 100 20.7 42.9 9121102 70.7 100 22.7 40.4 9121110 47.2 100 29.2 29.9 Appendices |173 NODE IDCOLONIAL AND SEMICOLONIAL WADERS(F) CRYPTIC WADERS* (F)SHOREBIRDS(F/M)SWIMMERS, DIVERS ANDGRAZERS(F) 9121111 64.9 100 39.7 56.1 9121120 59.7 0 20 25.4 9121130 82.2 100 11.4 78.2 9121131 77.0 100 47.0 71.3 9121132 77.0 100 47.0 71.3 9121150 76.9 0 13.6 32.9 9121151 55.3 100 22.1 22.0 9121152 66.9 100 20.8 43.0 9121160 67.7 0 23.5 42.6 9121161 80.4 100 72.5 87.9 9129042 74.9 100 100 65.3 9130010 97.2 100 45.3 77.3 9130011 55.5 0 33.6 62.8 9130012 65.6 0 26.8 40.7 9130030 71.7 0 17.5 45.6 9130040 75.6 0 19.1 40.8 9130050 65.2 100 30.8 59.3 9130060 61.3 0 29.1 48.2 9130061 60.6 0 29.7 48.9 9130070 92.5 0 54.6 100 9130080 72.5 0 12.7 39.9 9130090 62.1 0 26.7 39.2 9130091 76.4 0 29.2 50.3 9130100 80.2 0 16.6 46.8 9130110 76.4 0 22.3 65.8 9130111 96.0 0 57.7 97.6 9130140 82.3 100 42.2 72.6 9130150 70.6 0 24.5 46.6 9139000 84.2 100 21.1 57.9 *Distributionforthis assetwas developed using habitat associationssincespeciesdistribution models were notavailable. 174|Southern Gulf catchments ecological assessment Apx TableA-3River systemmodel nodes used for eachofprawns, turtles and other species The combined end-of-system river system model node 9100000is the designated end-of-system node unlessotherwise stated.9100000. This EOS node combined flows from the Nicholson (9121090 and 9129040) and theLeichhardt (9130071)outlets.F= Freshwater; M= Marine and F/M = Freshwater and Marine. NODE IDBANANA PRAWNS* (M) ENDEAVOURPRAWNS* (M) TIGER PRAWNS* (M) FRESHWATERTURTLES(F) MUD CRABS* (M) 9100000 100 100 100 0 100 9121010 0 0 0 63.4 0 9121011 0 0 0 47.4 0 9121012 0 0 0 65.1 0 9121013 0 0 0 48.6 0 9121014 0 0 0 65.2 0 9121015 0 0 0 88.2 0 9121016 0 0 0 87.6 0 9121020 0 0 0 41.3 0 9121030 0 0 0 52.7 0 9121031 0 0 0 42.1 0 9121032 0 0 0 47.6 0 9121033 0 0 0 73.0 0 9121034 0 0 0 86.8 0 9121035 0 0 0 86.7 0 9121040 0 0 0 52.6 0 9121041 0 0 0 83.0 0 9121050 0 0 0 62.7 0 9121051 0 0 0 65.9 0 9121052 0 0 0 53.6 0 9121053 0 0 0 47.3 0 9121060 0 0 0 63.5 0 9121061 0 0 0 83.0 0 9121070 0 0 0 68.5 0 9121071 0 0 0 56.2 0 9121072 0 0 0 58.0 0 9121073 0 0 0 49.7 0 9121074 0 0 0 49.7 0 9121075 0 0 0 58.0 0 9121080 0 0 0 31.8 0 9121100 0 0 0 43.4 0 9121101 0 0 0 42.4 0 9121102 0 0 0 39.9 0 9121110 0 0 0 52.0 0 Appendices |175 NODE IDBANANA PRAWNS* (M) ENDEAVOURPRAWNS* (M) TIGER PRAWNS* (M) FRESHWATERTURTLES(F) MUD CRABS* (M) 9121111 0 0 0 49.4 0 9121120 0 0 0 46.1 0 9121130 0 0 0 62.1 0 9121131 0 0 0 86.2 0 9121132 0 0 0 86.2 0 9121150 0 0 0 20.7 0 9121151 0 0 0 38.9 0 9121152 0 0 0 42.2 0 9121160 0 0 0 58.9 0 9121161 0 0 0 100 0 9129042 0 0 0 98.2 0 9130010 0 0 0 40.4 0 9130011 0 0 0 35.3 0 9130012 0 0 0 39.3 0 9130030 0 0 0 50.1 0 9130040 0 0 0 52.3 0 9130050 0 0 0 37.3 0 9130060 0 0 0 40.5 0 9130061 0 0 0 40.5 0 9130070 0 0 0 82.0 0 9130080 0 0 0 46.4 0 9130090 0 0 0 39.0 0 9130091 0 0 0 40.7 0 9130100 0 0 0 52.1 0 9130110 0 0 0 43.3 0 9130111 0 0 0 71.1 0 9130140 0 0 0 50.1 0 9130150 0 0 0 43.5 0 9139000 0 0 0 64.6 0 *Distributionforthis assetwas developed using habitat associationssincespecies distribution models were notavailable. 176|Southern Gulf catchments ecological assessment Apx TableA-4River systemmodel nodes used for eachoffreshwater-dependent habitats The combined end-of-system river system model node 9100000is the designated end-of-system node unlessotherwise stated.9100000. This EOS node combined flows from the Nicholson (9121090 and 9129040) and theLeichhardt (9130071) outlets.F= Freshwater; M= Marine;F/M = Freshwater and Marine andF/T= Freshwater andTerrestrial. NODE IDFLOODPLAINWETLANDS(F) INCHANNELWATERHOLES(F) MANGROVES(M) SALTPANS ANDSALT FLATS(M) SEAGRASS (M)SURFACEWATERDEPENDENTVEGETATION(F/T) 9100000 0 0 100 100 100 0 9121010 100 100 0 0 0 100 9121011 0 100 0 0 0 100 9121012 100 100 0 0 0 100 9121013 100 100 0 0 0 100 9121014 100 100 0 0 0 100 9121015 100 100 0 0 0 100 9121016 100 0 0 0 0 100 9121020 100 100 0 0 0 100 9121030 0 100 0 0 0 100 9121031 100 100 0 0 0 100 9121032 0 100 0 0 0 100 9121033 100 100 0 0 0 100 9121034 0 100 0 0 0 100 9121035 0 100 0 0 0 100 9121040 0 100 0 0 0 100 9121041 100 100 0 0 0 100 9121050 100 100 0 0 0 100 9121051 100 100 0 0 0 100 9121052 100 100 0 0 0 100 9121053 100 100 0 0 0 100 9121060 100 100 0 0 0 100 9121061 100 100 0 0 0 100 9121070 0 100 0 0 0 100 9121071 100 100 0 0 0 100 9121072 0 100 0 0 0 100 9121073 0 100 0 0 0 100 9121074 0 100 0 0 0 100 9121075 0 100 0 0 0 100 9121080 100 100 0 0 0 100 9121100 100 100 0 0 0 100 9121101 100 100 0 0 0 100 Appendices |177 NODE IDFLOODPLAINWETLANDS(F) INCHANNELWATERHOLES(F) MANGROVES(M) SALTPANS ANDSALT FLATS(M) SEAGRASS (M)SURFACEWATERDEPENDENTVEGETATION(F/T) 9121102 100 100 0 0 0 100 9121110 100 100 0 0 0 100 9121111 100 100 0 0 0 100 9121120 0 100 0 0 0 100 9121130 100 100 0 0 0 100 9121131 100 100 0 0 0 100 9121132 100 100 0 0 0 100 9121150 0 100 0 0 0 100 9121151 100 100 0 0 0 100 9121152 100 100 0 0 0 100 9121160 0 100 0 0 0 100 9121161 100 0 0 0 0 100 9129042 100 0 0 0 0 100 9130010 100 100 0 0 0 100 9130011 0 100 0 0 0 100 9130012 0 100 0 0 0 100 9130030 0 100 0 0 0 100 9130040 0 100 0 0 0 100 9130050 100 100 0 0 0 100 9130060 0 100 0 0 0 100 9130061 0 100 0 0 0 100 9130070 0 0 0 0 0 100 9130080 0 100 0 0 0 100 9130090 0 100 0 0 0 100 9130091 0 100 0 0 0 100 9130100 0 100 0 0 0 100 9130110 0 0 0 0 0 100 9130111 0 100 0 0 0 100 9130140 100 100 0 0 0 100 9130150 0 100 0 0 0 100 9139000 100 100 0 0 0 100 178|Southern Gulf catchments ecological assessment Mean changes in catchment flow for specific species and habitats for selected assets across different water harvesting scenarios P3428#yIS1 Apx Figure B-1 Mean changes in important metrics for representative assets are shown across water harvesting increments, varying by system target and pump-start threshold with no annual diversion commencement flow requirement and pump rate of 30 days Colour intensity represents the mean level of change in important flow dependencies with the scenario given the habitat importance of each node for each asset. P3431#yIS1 Apx Figure B-2 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and pump-start threshold for an annual diversion commencement flow requirement of 250 GL and pump rate of 30 days Colour intensity represents the mean level of change in important flow dependencies with the scenario given the habitat importance of each node for each asset. P3434#yIS1 Apx Figure B-3 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and a pump threshold of 200 ML/day and a pump rate of 30 days Colour intensity represents the mean level of change occurring in the asset’s important flow metrics with the scenarios given the habitat importance of each node for each asset. P3437#yIS1 Apx Figure B-4 Mean change associated with each asset’s important flow dependencies across water harvesting increments of system target and pump threshold of 200 ML/day and a pump rate of 30 days and no annual diversion commencement flow requirement Colour intensity represents the mean level of change occurring in the asset’s important flow dependencies with the scenarios given the habitat importance of each node for each asset. Asset hydrometricsand their weightingsin flowdependenciesmodelling Asset specific hydrometrics were selected toreflect key aspects of habitat function, life-history, and flow-ecology relationships foreach asset.An overview of flow dependenciesand flow ecologyfor each asset isprovided in Merrin et al. (2024).Metrics selected and used in the analysis areprovided inApx TableC-5with definitions. Apx TableC-1Hydrometrics selected as important forfish,sharks and raysalong withtheirrespectiveweightings METRICSBARRAMUNDICATFISHGRUNTERMULLETSAWFISHTHREADFIN exceedQ10.1 1 exceedQ75.1 1 exceedQ90.1 1 1 exceedQ99.1 1 1 highQduration01singleSpell1 1 1 JDayAnMax1 1 1 1 1 JDayAnMin1 1 1 1 lowQDuration75singleSpell1 1 1 1 lowQDuration90singleSpell1 1 1 1 1 lowQDuration99singleSpell1 1 1 1 maxQrelativeToMeanDailyQ 1 meanAnMaxMov3 1 1 meanAnMaxMov30 1 1 1 meanAnMaxMov7 1 1 1 meanAnMaxMov90 1 1 meanAnMinMov31 1 1 1 meanAnMinMov301 1 1 1 1 1 meanAnMinMov71 1 1 1 meanAnMinMov90 1 1 meanAnZeroFlowDays1 1 meanQ1 meanQ011 1 1 1 meanQ021 1 1 1 184|Southern Gulf catchments ecological assessment METRICSBARRAMUNDICATFISHGRUNTERMULLETSAWFISHTHREADFIN meanQ031 1 1 1 meanQ041 meanQ051 meanQ061 meanQ071 meanQ081 1 meanQ091 1 1 meanQ101 1 1 1 1 meanQ111 1 1 1 meanQ121 1 1 1 medQ1 1 seasonMeanQ11 seasonMeanQ31 seasonMeanQ41 skewnessQ1 specificMeanAnMax1 specificMeanQ1 Appendices |185 Apx TableC-2Hydrometrics selected as important for each of the waterbirds, along with their respective weightings METRICSCOLONIAL AND SEMICOLONIAL WADERSCRYPTIC WADERSSHOREBIRDSSWIMMERS, DIVERSAND GRAZERS exceedQ1.1 exceedQ10.1 0.6 0.5 0.8 exceedQ25.1 0.8 0.5 exceedQ75.1 0.8 exceedQ90.0.8 fallRate1 0.6 0.9 1 highQduration01singleSpell1 highQduration01Total0.8 highQduration10singleSpell1 0.5 highQduration10Total0.8 0.6 highQduration25singleSpell1 0.5 1 highQduration25Total0.7 1 JDayAnMax0.8 JDayAnMin0.3 lowQDuration90singleSpell0.5 lowQDuration90Total1 lowQDuration99singleSpell1 maxQ0.5 0.6 meanAnMaxMov30 0.8 0.8 0.5 meanAnMaxMov7 0.8 0.5 0.2 meanAnMaxMov90 0.8 0.9 meanAnMinMov300.7 0.8 1 meanAnMinMov900.5 0.5 1 meanQ0.5 0.3 meanQ010.5 meanQ100.5 meanQ110.5 meanQ120.5 medQ0.7 1 minQ0.7 riseRate0.6 0.8 0.5 seasonMeanQ40.6 specificMeanAnMax 0.6 specificMeanAnMin0.6 0.5 1 specificMeanQ 0.8 0.5 specificMedQ0.8 0.5 1 186|Southern Gulf catchments ecological assessment Apx TableC-3Hydrometrics selected as important for each of theprawns, turtlesandother species, alongwith their respective weightings METRICSBANANAPRAWNSENDEAVOURPRAWNSTIGER PRAWNSFRESHWATERTURTLESMUD CRABS exceedQ1.1 exceedQ10.1 exceedQ75.1 exceedQ90.1 exceedQ99.1 fallRate0.8 highQduration01singleSpell1 highQduration10singleSpell1 JDayAnMax0.5 1 JDayAnMin1 1 0.7 1 lowQDuration75singleSpell1 1 lowQDuration75Total1 1 lowQDuration90singleSpell1 lowQDuration99singleSpell1 meanAnMaxMov3 1 1 meanAnMaxMov30 1 0.5 meanAnMaxMov7 1 1 meanAnMaxMov90 1 meanAnMinMov301 1 1 1 1 meanAnMinMov901 1 1 meanAnZeroFlowDays1 1 meanQ1 0.6 meanQ011 1 1 0.9 1 meanQ021 0.8 1 meanQ031 1 meanQ091 meanQ101 0.7 1 meanQ111 0.8 1 meanQ121 1 1 0.9 1 medQ1 1 riseRate0.5 seasonMeanQ10.5 seasonMeanQ41 specificMeanAnMax0.5 Appendices |187 Apx TableC-4Hydrometrics selected as important for each of the freshwater-dependent habitats, along with theirrespective weightings NODE IDFLOODPLAINWETLANDSINCHANNELWATERHOLESMANGROVESSALTPANSAND SALTFLATSSEAGRASSSURFACEWATERDEPENDENTVEGETATION exceedQ1.1 1 1 1 1 exceedQ10.1 1 exceedQ25.1 exceedQ75.1 exceedQ90.1 exceedQ99.0.5 fallRate0.5 0.4 highQduration01singleSpell0.8 1 1 1 highQduration01Total1 highQduration10singleSpell0.8 1 1 highQduration25singleSpell1 JDayAnMax0.4 1 0.2 JDayAnMin0.8 lowQDuration75singleSpell0.8 lowQDuration75Total 0.2 lowQDuration90singleSpell0.9 lowQDuration90Total 0.5 lowQDuration99singleSpell1 1 maxQ1 maxQrelativeToMeanDailyQ0.5 meanAnMaxMov3 0.6 1 1 1 meanAnMaxMov30 0.6 1 1 meanAnMaxMov7 0.6 1 0.6 meanAnMaxMov90 0.4 meanAnMinMov3 0.1 1 meanAnMinMov301 meanAnMinMov7 0.3 188|Southern Gulf catchments ecological assessment NODE IDFLOODPLAINWETLANDSINCHANNELWATERHOLESMANGROVESSALTPANSAND SALTFLATSSEAGRASSSURFACEWATERDEPENDENTVEGETATION meanAnMinMov901 meanAnZeroFlowDays 1 meanQ010.6 1 1 meanQ020.6 meanQ030.6 meanQ100.6 meanQ110.6 1 meanQ120.6 1 1 minQ1 minQ05 0.2 minQ060.2 minQ07 0.2 minQ080.5 minQ09 0.8 minQ101 minQ11 1 minQ120.8 riseRate0.8 seasonMeanQ41 specificMeanAnMax1 specificMeanQ1 0.6 specificMedQ1 volHighQ1x1 Appendices |189 Metricsarethose usedfor asset analysisdrawnfrom a longer list of metrics. Metrics are selectedbased upon asset flow-ecology and consider flow-relationships and needsand thosethat can becalculated on an annualbasis (i.e. not multi-year such as annual recurrence intervals). An overviewof flowdependenciesand flow ecologyfor each asset isprovided inMerrin et al. (2024). Apx TableC-5The hydrometrics selected as important foreach of the ecological assets HYDROMETRICDEFINITION meanAnIndFloodDurationMean annual independent flood pulse durationmeanQMean daily flowsmedQMediandaily flowspecificMeanQMean flows divided bycatchment areaspecificMedQMedian flows divided by catchment areacvMeanQCoefficient of variation in dailyflowskewnessQSkewness indaily flowsmeanQ01Mean JanuarydischargemeanQ02Mean FebruarydischargemeanQ03Mean MarchdischargemeanQ04Mean AprildischargemeanQ05Mean MaydischargemeanQ06Mean JunedischargemeanQ07Mean JulydischargemeanQ08Mean AugustdischargemeanQ09Mean SeptemberdischargemeanQ10Mean OctoberdischargemeanQ11Mean NovemberdischargemeanQ12Mean DecemberdischargeseasonMeanQ1Mean SpringdischargeseasonMeanQ2Mean SummerdischargeseasonMeanQ3Mean AutumndischargeseasonMeanQ4Mean WinterdischargeexceedQ75Low flood pulse count (<75th percentile) exceedQ90Low floodpulse count (<90th percentile) exceedQ99Low flood pulse count (<99th percentile) specificMeanAnMinMean annual minimum flows divided by catchment areaexceedQ1High flood pulse count 1 (1th percentile) exceedQ10High flood pulse count 1 (10th percentile) exceedQ25High flood pulse count 1 (25th percentile) specificMeanAnMaxMean annual maximum flows dividedby catchment area meanAnIndOverbankFloodDurationMean annual independent overbank flood pulse durationmeanAnFloodDurationMean annual flood flow pulse duration 190|Southern Gulf catchments ecological assessment HYDROMETRICDEFINITION bankfullQMean annual flood volume with respect to bankfull volume volHighQ1xMean of the high flow volume (calculated asthe area between the hydrograph and the upperthreshold during the high flow event) volHighQ3xMean of the high flow volume (calculated asthe area between the hydrograph and the upperthreshold during the high flow event) volHighQ7xMean of the high flow volume (calculated asthe area between the hydrograph and the upperthreshold during the high flow event) meanAnMinMov1Annual minima of 1-day means of daily dischargemeanAnMinMov3Annual minima of3-day means of daily discharge meanAnMinMov7Annual minima of7-day means of daily dischargemeanAnMinMov30Annual minima of30-day meansof daily dischargemeanAnMinMov90Annual minima of90-day meansof daily dischargelowQDuration75Low flow pulse duration (75th percentile) lowQDuration90Low flow pulse duration (90th percentile) lowQDuration99Low flow pulse duration (99th percentile) meanAnZeroFlowDaysMean annual number of days having zero daily flowmeanAnMaxMov1 Annual maxima of 1-day means of daily discharge meanAnMaxMov3 Annual maxima of3-day means of daily dischargemeanAnMaxMov7 Annual maxima of7-day means of daily discharge meanAnMaxMov30 Annual maxima of30-day meansof daily dischargemeanAnMaxMov90 Annual maxima of90-day meansof daily discharge highQduration25High flow pulse duration (25th percentile) highQduration10High flow pulse duration (10th percentile) highQduration01High flow pulse duration (1stpercentile) seasonalityMeanQSeasonality (M/P) of mean daily flow (month) perennialityPerreniality-% contribution to mean annual discharge by the six driest months ofthe year JDayAnMinJulian date of annual minimum seasonalityMinQSeasonality (M/P) of minimum instantaneous flow (month) JDayAnMaxJulian date of annual maximum seasonalityMaxQSeasonality (M/P) of maximum instantaneous flow (month) riseRateRise rate- Mean rate of positivechanges in flow from one day to the next fallRateFall rate- Mean rate of negativechanges in flow from one day to the next revPerYearNumber of reversals -Number of negative and positive changes in water conditionsfrom one day to the next Appendices |191 Asset metrics with the largest contribution to changes in asset flow dependencies by scenario The following tables show the most altered location (node) and associated metrics for all the ecological assets under the different water management scenarios. The hydrometric values should be considered as an indicator of the level of hydrological change occurring within the key components of the hydrograph important for each asset. Considering where change occurs across the different flow components facilitates an understanding of where change is most significant in association with the different scenarios for each asset. Apx Table D-1 Most changed metric at the most altered location (node) in Scenario B-WT150P200R30E0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4852#yIS1 Apx Table D-2 Most changed metric at the most altered location (node) in Scenario B-WT150P200R30E150 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4855#yIS1 Apx Table D-3 Most changed metric at the most altered location (node) in Scenario B-WT150P200R30E250 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4859#yIS1 Apx Table D-4 Most changed metric at the most altered location (node) in Scenario B-WT50P600R30E0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4863#yIS1 Apx Table D-5 Most changed metric at the most altered location (node) in Scenario B-WT50P600R30E150 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4867#yIS1 Apx Table D-6 Most changed metric at the most altered location (node) in Scenario B-WT50P600R30E250 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4871#yIS1 Apx Table D-7 Most changed metric at the most altered location (node) in Scenario B-WT150P600R30E0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4875#yIS1 Apx Table D-8 Most changed metric at the most altered location (node) in Scenario B-WT150P600R30E150 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4879#yIS1 Apx Table D-9 Most changed metric at the most altered location (node) in Scenario B-WT150P600R30E250 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4883#yIS1 Apx Table D-10 Most changed metric at the most altered location (node) in Scenario B-WT300P600R30E0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4887#yIS1 Apx Table D-11 Most changed metric at the most altered location (node) in Scenario B-WT300P600R30E150 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4891#yIS1 Apx Table D-12 Most changed metric at the most altered location (node) in Scenario B-WT300P600R30E250 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4895#yIS1 Apx Table D-13 Most changed metric at the most altered location (node) in Scenario B-DGPC for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4899#yIS1 Apx Table D-14 Most changed metric at the most altered location (node) in Scenario B-DGPCT for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4903#yIS1 Apx Table D-15 Most changed metric at the most altered location (node) in Scenario B-DGR for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4907#yIS1 Apx Table D-16 Most changed metric at the most altered location (node) in Scenario B-DGRT for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4911#yIS1 Apx Table D-17 Most changed metric at the most altered location (node) in Scenario B-D2 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4915#yIS1 Apx Table D-18 Most changed metric at the most altered location (node) in Scenario B-D2T for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. Apx Table D-19 Most changed metric at the most altered location (node) in Scenario CEdry for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4923#yIS1 Apx Table D-20 Most changed metric at the most altered location (node) in Scenario D-WT50P600R30E0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4927#yIS1 Apx Table D-21 Most changed metric at the most altered location (node) in Scenario D-WT150P600R30E0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4931#yIS1 Apx Table D-22 Most changed metric at the most altered location (node) in Scenario D-WT300P600R30E0 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4935#yIS1 Apx Table D-23 Most changed metric at the most altered location (node) in Scenario D-D2 for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4939#yIS1 Apx Table D-24 Most changed metric at the most altered location (node) in Scenario D-D2T for ecological assets The combined metric change (%ile) shows the percentile shift for each asset, while the three highest-ranked metrics (Rank 1, Rank 2, and Rank 3) indicate the key drivers of this change in each scenario. For Rank 1, the proportion of change represents the influence of the highest ranked flow metric on overall change. For the definition of the metrics please refer to Apx Table C-5. P4943#yIS1 Waterbird groups and their species To provide a simple basis for understanding and communicating the associated risks and opportunities for waterbirds related to potential water resource development in northern Australia, waterbird species have been grouped into four high-level groups. These groups are based on foraging behaviour and habitat dependencies, and nesting behaviour and habitat dependencies. Both foraging and nesting dependencies need to be taken into account, because while some species both forage and nest in northern Australia, others migrate annually to take advantage of foraging opportunities and avoid the northern hemisphere winter. The four waterbird groups are: 1. Colonial and semi-colonial nesting waders 2. Shorebirds 3. Cryptic waders 4. Swimming, diving and grazing waterbirds. Group 1: Colonial and semi-colonial nesting waders (Apx Table E-1). Colonial and semi-colonial wading species have a high level of dependence on flood timing, extent, duration, depth, vegetation type and condition for breeding. They are also often dependent on specific important breeding sites in Australia. They are usually easily detectable when breeding and good datasets are available for most species. These species are typically nomadic or partially migratory. Group 2: Cryptic waders (Apx Table E-2). Cryptic wading species have a high level of dependence on shallow temporary and permanent wetland habitats with relatively dense emergent aquatic vegetation that requires regular or ongoing inundation to survive (e.g. reeds, rushes, sedges, wet grasses and lignum). These species breed in Australia and usually nest as independent pairs though some may occasionally nest semi-colonially. They may be sedentary, nomadic, migratory or partially migratory. Few data are available; however, habitat requirements can be used as surrogates to assess vulnerability. Group 3: Shorebirds (Apx Table E-3). Shorebirds have a high level of dependence on end-of- system flows and large inland flood events that provide broad areas of very shallow water and mudflat type environments. They occur across freshwater and marine habitats and are largely migratory or nomadic, mostly breed in the northern hemisphere rather than Australia, and are a group of international concern. Group 4: Swimming, diving and grazing waterbirds (Apx Table E-4). These are species with a relatively high level of dependence on semi-open, open and deeper water environments, who commonly swim when foraging (including diving, filtering, dabbling, grazing) or when taking refuge. These species breed in Australia and may be sedentary, nomadic, migratory or partially migratory. Apx TableE-1Species in the colonial and semi-colonial nesting wading waterbird group, and their national andinternational conservation status (LC=Least concern) SPECIES NAMESPECIES SCIENTIFIC NAMEFAMILY SCIENTIFIC NAMEIUCN STATUS Australian white ibisThreskiornis moluccusThreskiornithidaeLCBanded stiltCladorhynchus leucocephalusRecurvirostridaeLCBlack-winged stilt (piedHimantopus himantopus (HimantopusRecurvirostridaeLCstilt)leucocephalus) Cattle egretBubulcus ibis (Ardea ibis)ArdeidaeLCEastern reef egretEgretta sacraArdeidaeLCGlossy ibisPlegadis falcinellusThreskiornithidaeLCGreat Egret (eastern great Ardea alba (Ardea modesta, Ardea alba ArdeidaeLCegret)modesta) Great-billed heronArdea sumatranaArdeidaeLCIntermediate egretArdea intermediaArdeidaeLCLittle egretEgretta garzettaArdeidaeLCNankeen night-heronNycticorax caledonicusArdeidaeLCPied heronEgretta picata (Ardea picata)ArdeidaeLCRed-necked avocetRecurvirostra novaehollandiaeRecurvirostridaeLCRoyal spoonbillPlatalea regiaThreskiornithidaeLCSarus craneGrusantigoneGruidaeVulnerableStraw-necked ibisThreskiornis spinicollisThreskiornithidaeLCWhite-faced heronEgrettanovaehollandiaeArdeidaeLCWhite-necked heronArdea pacificaArdeidaeLCYellow-billed spoonbillPlatalea flavipesThreskiornithidaeLCBlack-necked storkEphippiorhynchus asiaticusCiconiidaeLCBrolgaAntigone rubicundaGruidaeLC Appendices |217 Apx TableE-2Species in the cryptic wadingwaterbird group, and their national and international conservationstatus (LC=Least concern) SPECIES NAMESPECIES SCIENTIFIC NAMEFAMILY SCIENTIFICNAMEIUCN STATUS Australianlittle bitternIxobrychus dubius (Ixobrychus minutus)ArdeidaeLCAustralianpainted snipeRostratula australisRostratulidaeEndangeredAustralian spotted crakePorzana flumineaRallidaeLCBaillon’s crakePorzana pusilla (Zapornia pusilla)RallidaeLCBlack bitternIxobrychus flavicollisArdeidaeLCBuff-banded railHypotaenidia philippensisRallidaeLCChestnut railEulabeornis castaneoventris (GallirallusRallidaeLCcastaneoventris) Latham’s snipeGallinagohardwickiiScolopacidaeLCLewin’s railLewinia pectoralisRallidaeLCRed-necked crakeRallina tricolorRallidaeLCSpotless crakeZapornia tabuensis (Porzana tabuensis)RallidaeLCStriated heronButorides striatus (Butorides striata)ArdeidaeLCWhite-browed crakeAmaurorniscinerea (Poliolimnas cinereus)RallidaeLC 218|Southern Gulf catchments ecological assessment Apx TableE-3Species in the shorebirdsgroup, and their national and international conservation status LC=Least concern) SPECIES NAMESPECIES SCIENTIFICNAMEFAMILYSCIENTIFIC NAMEPOPULATIONTYPEIUCN STATUSAUSTRALIAN CONSERVATION STATUS AustralianStiltia isabellaGlareolidaeAustralianLCLCpratincoleBeach stone-Esacus BurhinidaeAustralianNear threatenedLCcurlewmagnirostrisMaskedVanellus milesCharadriidaeAustralianLCLClapwingRed-cappedCharadriusCharadriidaeAustralianLCLCploverruficapillusBlack-frontedElseyornisCharadriidaeEndemicLCLCdotterelmelanops Inland dotterelCharadriusCharadriidaeEndemicLCLCaustralis (Peltohyasaustralis) Red-kneedErythrogonysCharadriidaeEndemicLCLCdotterelcinctus BandedVanellus tricolorCharadriidaeEndemicLCLClapwingBar-tailedLimosa lapponicaScolopacidaeNon-breedingLCCritically endangeredgodwitmigrantBlack-tailedLimosa limosaScolopacidaeNon-breedingNear threatenedNear threatenedgodwitmigrantBroad-billedLimicola falcinellusScolopacidaeNon-breedingLCLCsandpipermigrantCommon Tringa nebulariaScolopacidaeNon-breedingLCLCgreenshankmigrantCommon Actitis hypoleucosScolopacidaeNon-breedingLCLCsandpipermigrantCurlewCalidris ferrugineaScolopacidaeNon-breedingVulnerableCritically endangeredsandpipermigrantEastern curlewNumeniusScolopacidaeNon-breedingVulnerableCritically endangeredmadagascariensismigrant Great knotCalidris tenuirostrisScolopacidaeNon-breedingVulnerableCritically endangeredmigrantGreater sandCharadriusCharadriidaeNon-breedingLCLCplover,largeleschenaultiimigrantsand ploverGrey ploverPluvialis squatarolaCharadriidaeNon-breedingLCLCmigrantGrey-tailedTringa brevipesScolopacidaeNon-breedingNear threatenedLCtattlermigrant Lesser sandCharadriusCharadriidaeNon-breedingLCCritically endangeredplovermongolusmigrantLittle curlewNumenius minutusScolopacidaeNon-breedingLCLCmigrant Appendices |219 SPECIES NAMESPECIES SCIENTIFICNAMEFAMILYSCIENTIFIC NAMEPOPULATIONTYPEIUCN STATUSAUSTRALIAN CONSERVATION STATUS Long-toed stintCalidris subminutaScolopacidaeNon-breedingLCLCmigrantMarshTringa stagnatilisScolopacidaeNon-breedingLCLCsandpipermigrantOriental Charadrius veredusCharadriidaeNon-breedingLCLCplover,migrantOriental dotterelOriental GlareolaGlareolidaeNon-breedingLCLCpratincolemaldivarummigrant Pacific goldenPluvialis fulvaCharadriidaeNon-breedingLCLCplovermigrantRed knotCalidris canutusScolopacidaeNon-breedingVulnerableCritically endangeredmigrantRed-neckedCalidris ruficollisScolopacidaeNon-breedingLCNear threatenedstintmigrantRuddyArenaria interpresScolopacidaeNon-breedingNear threatenedNear threatenedturnstonemigrantSanderlingCalidris albaScolopacidaeNon-breedingLCLCmigrantSharp-tailedCalidris acuminataScolopacidaeNon-breedingLCLCsandpipermigrantSwinhoe’s Gallinago megalaScolopacidaeNon-breedingLCLCsnipemigrantTerekXenuscinereusScolopacidaeNon-breedingLCLCsandpipermigrantWhimbrelNumeniusScolopacidaeNon-breedingLCLCphaeopusmigrantWood Tringa glareolaScolopacidaeNon-breedingLCLCsandpipermigrantAsianLimnodromusScolopacidaeNon-breedingNear threatenedNear threateneddowitchersemipalmatusmigrantCommon Tringa totanusScolopacidaeNon-breedingLCLCredshank,migrantRedshankDouble-CharadriusCharadriidaeNon-breedingLCLCbanded ploverbicinctusmigrantPectoralCalidrismelanotosScolopacidaeNon-breedingLCLCsandpipermigrant WanderingTringa incana ScolopacidaeNon-breedingLCLCtattler(Heteroscelus migrantincanus) Little ringedCharadrius dubiusCharadriidaeNon-breedingLCLCplovermigrantPin-tailedGallinago stenuraScolopacidaeNon-breedingLCData deficientsnipemigrant 220|Southern Gulf catchments ecological assessment SPECIES NAMESPECIES SCIENTIFICNAMEFAMILYSCIENTIFIC NAMEPOPULATIONTYPEIUCN STATUSAUSTRALIAN CONSERVATION STATUS Red-neckedPhalaropus lobatusScolopacidaeNon-breedingLCLCphalaropemigrantRuff (Reeve)Calidris pugnaxScolopacidaeNon-breedingLCLC(Philomachusmigrantpugnax) Baird’s Calidris bairdiiScolopacidaeVagrantLCLCsandpiperCaspian ploverCharadriusCharadriidaeVagrantLCLCasiaticusGreenTringa ochropusScolopacidaeVagrantLCLCsandpiper Buff-breastedTryngitesScolopacidaeVagrantNearthreatenedLCsandpipersubruficollisDunlinCalidris alpinaScolopacidaeVagrantLCLCGrey (red)PhalaropusScolopacidaeVagrantLCLCphalaropefulicariaLesserTringa flavipesScolopacidaeVagrantLCLCyellowlegsLittle stintCalidris minutaScolopacidaeVagrantLCLCCommon CharadriusCharadriidaeVagrantLCLCringed ploverhiaticulaSpottedTringa erythropusScolopacidaeVagrantLCLCredshankWhite-rumpedCalidris fuscicollisScolopacidaeVagrantLCLCsandpiper Appendices |221 Apx TableE-4Species in the swimming, diving and grazingwaterbirds group, and their national and internationalconservation status (LC=Least concern) SPECIES NAMESPECIES SCIENTIFIC NAMEFAMILY SCIENTIFICNAMEPOPULATIONTYPEIUCN STATUSAUSTRALIAN CONSERVATION STATUS AustralianAnas rhynchotis (Spatula AnatidaeAustralianLCLC(Australasian)rhynchotis) shovelerAustralian woodChenonetta jubataAnatidaeEndemicLCLCduck (manedduck) Spotted whistling-Dendrocygna guttataAnatidaeAustralianLCLCduckGarganey,Spatula querquedula (AnasAnatidaeNon-breedingLCLCGarganey tealquerquedula)migrantFreckled duckStictonetta naevosaAnatidaeEndemicLCLCChestnut tealAnas castaneaAnatidaeEndemicLCLCGrey tealAnas gracilisAnatidaeAustralianLCLCPacific black duckAnas superciliosaAnatidaeAustralianLCLCHardheadAythya australisAnatidaeAustralianLCLCBlack swanCygnus atratusAnatidaeEndemicLCLCWanderingDendrocygna arcuateAnatidaeAustralianLCLCwhistling-duckPlumed whistling-Dendrocygna eytoniAnatidaeAustralianLCLCduckPink-eared duckMalacorhynchusAnatidaeEndemicLCLCmembranaceusCotton pygmy-Nettapus coromandelianusAnatidaeAustralianLCLCgooseGreen pygmy-Nettapus pulchellusAnatidaeAustralianLCLCgooseBlue-billed duckOxyura australisAnatidaeEndemicLCLCRadjah shelduckRadjah radjah (TadornaAnatidaeAustralianLCLCradjah) AustralianTadorna tadornoidesAnatidaeEndemicLCLCshelduckAustralasianAnhinga novaehollandiaeAnhingidaeAustralianLCLCdarterMagpie gooseAnseranas semipalmataAnseranatidaeAustralianLCLCComb-crestedIrediparra gallinaceanJacanidaeAustralianLCLCjacanaCommon noddyAnous stolidusLaridaeAustralianLCLCWhiskered ternChlidonias hybridaLaridaeAustralianLCLCWhite-wingedChlidonias leucopterusLaridaeNon-breedingLCLCblack ternmigrant 222|Southern Gulf catchments ecological assessment SPECIES NAMESPECIES SCIENTIFIC NAMEFAMILY SCIENTIFICNAMEPOPULATIONTYPEIUCN STATUSAUSTRALIAN CONSERVATION STATUS Silver gullChroicocephalusLaridaeAustralianLCLCnovaehollandiaeAustralian gull-Gelochelidon macrotarsaLaridaeEndemicLCLCbilled tern(breeding only) Common gull-Gelochelidon niloticaLaridaeNon-breedingLCLCbilled ternmigrantCaspian ternHydroprogne caspiaLaridaeAustralianLCLCBridled ternOnychoprion anaethetusLaridaeAustralianLCLCSooty ternOnychoprion fuscatusLaridaeAustralianLCLC(Onychoprion fuscata) Roseate ternSterna dougalliiLaridaeAustralianLCLCCommon ternSterna hirundoLaridaeNon-breedingLCLCmigrantBlack-naped ternSterna sumatranaLaridaeAustralianLCLCLittle ternSternula albifronsLaridaeAustralianLCLCLesser crested ternThalasseus bengalensisLaridaeAustralianLCLCCrestedternThalasseus bergiiLaridaeAustralianLCLCAustralian pelicanPelecanus conspicillatusPelicanidaeEndemicLCLC(breeding only) Little piedMicrocarbo melanoleucosPhalacrocoracidaeAustralianLCLCcormorantGreat cormorantPhalacrocorax carboPhalacrocoracidaeAustralianLCLCLittle black Phalacrocorax sulcirostrisPhalacrocoracidaeAustralianLCLCcormorantPied cormorantPhalacrocorax variusPhalacrocoracidaeAustralianLCLCGreat crestedPodicepscristatusPodicipedidaeAustralianLCLCgrebeHoary-headedPoliocephalus poliocephalusPodicipedidaeEndemicLCLCgrebeAustralasian grebeTachybaptus novaehollandiaePodicipedidaeAustralianLCLCPale-vented bush-AmaurornismoluccanaRallidaeAustralianLCLChen, Bush-hen Eurasian cootFulica atraRallidaeAustralianLCLCDusky moorhenGallinula tenebrosaRallidaeAustralianLCLCPurple swamphenPorphyrio porphyrioRallidaeAustralianLCLCBlack-tailedTribonyxventralisRallidaeEndemicLCLCnative-hen Appendices |223 As Australia’s nationalscience agency and innovation catalyst, CSIRO is solving the greatestchallenges through innovativescience and technology. CSIRO. Unlocking a better futurefor everyone. Contact us 1300 363 400+61 3 9545 2176csiroenquiries@csiro.aucsiro.au Forfurther informationEnvironment Dr Chris Chilcott+61 8 8944 8422chris.chilcott@csiro.au Environment Dr Cuan Petheram+61 3 6237 5669cuan.petheram@csiro.au Agriculture andFood Dr Ian Watson+61 7 4753 8606 Ian.watson@csiro.au